Atmosphere & Climate Posters

Earth-System models, forecasting climatic changes and their impacts, assemble the planetary system in model components interconnected by coupling interfaces. Atmosphere-ocean couplers, simulating greenhouse gas trades, use well-established generalizations requiring little calculus. Although satisfying computational constrains, some lack the ability to represent processes at finer resolutions for space and time.

We developed a framework to test 53 formulations for air-water gas exchange, the constants for virtually all gases being provided, and adapted it to a coupler for atmosphere (WRF) and ocean (WW3-NEMO) components. The classical solubility formulation was compared to a recent alternative relying in a different chemistry background. The classical transfer velocity formulation, using wind speed 10m above sea-surface, was compared to alternatives using the friction velocity, atmospheric stability, sea-surface agitation and wave breaking. Computational speed was greatly improved by calculus vectorization and parallelization.

We tested with simulated data relative to the Mediterranean and coastal North Atlantic. Differences between solubility formulations resulted in a bias of 3.86×10⁶ ton of CO₂, 880.7 ton of CH₄ and 401 ton of N₂O dissolved in the first meter below the sea-surface of the modelled region, corresponding to 5.9% of the N₂O yearly discharged by European estuaries. These differences focused in sensitive areas for Earth-System dynamics: the cooler polar waters and warmer, less-saline coastal waters. Differences between transfer velocity formulations resulted in 55.82% of the gas volume transferred over the sea-surface of the modelled region during the 66h simulated period. Again, many divergences occurred at the finer resolution of the coastal ocean.

The WW3-NEMO and WRF models were forced by observational data, also used for their calibration and validation. Remote sensing collected data about radiation, SST, SSS, wind and wave fields from missions ERS, Envisat, MSG, SMOS and Sentinel-1 (ESA) and Jason 2 (CNES-NASA). Testing the atmosphere-ocean coupler with satellite data also requires the Ocean Colour from Sentinel-3 and the near-surface concentrations of atmospheric trace gasses (GOSAT and OCO-2, for CO₂) provided with better accuracy and resolution than the MIPAS atmospheric sensor and SCIAMACHY spectrometer onboard Envisat. Our results can be used to improve the present algorithms inferring ocean pCO₂ and fluxes from satellite data.

The daily UV dose -- the integrated amount of solar UV radiation at the surface between sunrise and sunset -- provides important information for time-integrated health impacts of solar UV radiation, such as for example the beneficial production of Vitamin-D in the skin.

The incident solar UV radiation weighted with the action spectrum for Vitamin-D production in the human skin has slightly different dependencies on e.g. the solar zenith angle and the overhead ozone than the UV Index,  which is by definition calculated using the erythemal action spectrum related to the reddening of the skin.

Locally, the surface UV dose is determined by the solar zenith angle, the surface elevation and the time of the year, while important day-to-day modulations are related to variations in the overhead ozone layer and cloud cover. Most of the short-term variations can be estimated using satellite observations of the total ozone column and cloud cover.

The presentation describes our recent work on the extension and improvement of the TEMIS satellite-based Vitamin-D UV dose records. An important recent improvement for practical use has been a higher spatial resolution. The full data set is provided as a semi-operational service via http://www.temis.nl/uvradiation/. Once a day the TEMIS UV data sets are extended using satellite observations from the day before.

To estimate the daily UV dose we currently use MetOp-B/GOME-2 total ozone column and Meteosat Second Generation (MSG)/SEVIRI cloud cover observations. Our historical UV dose records, starting from the 1970s, are based on a total ozone column multi-sensor reanalysis (Van der A et al., 2010). MSG cloud cover observations are available since 2004.

A recent study conducted for Scotland indicates that there might be a statistically significant correlation between the local cumulative Vitamin-D UV dose and the Vitamin-D levels in representative blood samples of the population. The study therefore suggests that long time series of satellite-based Vitamin-D UV dose might be used in epidemiological research as a proxy for the Vitamin-D status in the population.

Also, recently Chipperfield et al. (2015), using an atmospheric chemistry-transport model, quantified the ozone depletion avoided by the implementation of the 1987 Montreal Protocol. By comparing the ozone columns of the world avoided with the historical ozone column record, we estimate the impact of the policy measures taken on the record of the incident UV radiation and specifically on the Vitamin-D UV dose, which is potentially relevant for health studies.

 

 

References:
    Van der A et al., 2010: "Multi sensor reanalysis of total ozone," Atmos. Chem. Phys. 10, 11277-11294.
    Chipperfield et al., 2015: "Quantifying the ozone and ultraviolet benefits already achieved by the Montreal Protocol," Nature Comm. 6:7233, 8pp.

In order to provide near-real-time monitoring of atmospheric contaminants, fast and reliable methods are required to detect anomalies in the atmospheric state. Full optimal estimation retrievals are computationally expensive, therefore, faster methods are needed to identify such anomalous events.

The Infrared Atmospheric Sounding Interferometer (IASI), on board both the MetOp-A and MetOp-B platforms, is a Fourier transform spectrometer covering the mid-infrared from 645-2760 cm⁻¹ (3.62-15.5 μm) with a spectral resolution of 0.5 cm⁻¹ (apodised) and a pixel diameter at nadir of 12 km. These characteristics allow global coverage to be achieved twice daily for each instrument, making IASI a very useful tool for the observation and tracking of atmospheric pollutants, large aerosol particles (such as desert dust) and volcanic plumes. The method shown makes full use of the spectral information from hyperspectral sounders and allows the presence of the target species to be determined in near-real-time (NRT), if required.

The analysis algorithms developed at the University of Oxford are well established for the flagging of volcanic ash and SO₂. These procedures have been extended to detect enhancements in additional atmospheric species, such as NH₃ and CO, which are important contaminants in pollution monitoring and forest fire detection. The data from the anomaly detection tests are available online via the IASI NRT website within 3 hours of measurement.     

An evaluation of a COSMO numerical weather prediction (NWP) model simulation of convective clouds with observed data derived from the geostationary meteorological satellite Meteosat Second Generation (MSG) is presented. The analysis was performed for five model runs, which simulated severe convective events occurring during the warm parts of the years 2012-2013. COSMO model was run with 2.8 km horizontal resolution and without parameterization of deep convection because the model is considered able to resolve and explicitly simulate at least the larger-scale elements of organized convection. The model used 50 vertical levels. The model set-up was completed by the assimilation of radar reflectivity and extrapolated radar reflectivity data based on the correction of the model water vapour mixing ratio. The length of the assimilation windows depended on the timing of the occurrence of convective events with minimal lengths of six hours. The study was extended by a forecasts computed by COSMO-CZ-EPS, an ensemble with explicit convection. The ensemble consists of 16 members computed by NWP model COSMO with same settings as the single COSMO run described above. The initial and boundary conditions were derived from COSMO-LEPS ensemble. The ensemble was run without and also with the radar reflectivity assimilation.

Synthetic satellite imageries were calculated using radiative transfer model RTTOV v11.2, which was implemented in the COSMO model. NWP model simulations of IR10.8 μm and WV06.2 μm brightness temperatures (BTs) with a horizontal resolution of 2.8 km were interpolated into the satellite projection and objectively verified against observations using a grid-to-grid approach in addition to spatial and object-based methods.

The results showed that the model reliably forecasts the locations of convective clouds associated with low BTs in both channels for the first 2-3 hours of forecasts. However, the quantitative values of the BTs were slightly higher than the MSG observational data. Moreover, the model well reflects cloud-free conditions. The analysis was completed by a verification of the precipitation forecasts using the same verification techniques. The scores were slightly worse than the synthetic satellite imageries forecasts, which is due to the fact that precipitation is more variable in space and that its resulting fields are less compact. In addition, we applied Pearson’s and Spearman’s correlation coefficients to determine and compare relationships between precipitation and cloudiness values represented by BTs for the observed and modelled data. The differences between the relationships are not large, and it can therefore be concluded that the performance of model microphysics is satisfactory.

The Climate Modelling User Group brings a modelling  perspective to ESA’s CCI programme. It does this through the application of CCI data sets in climate models and reanalyses, by developing data analysis tools, by maintaining a user dialogue on the ‘added value’ of CCI datasets and by engaging with key international actors. All this helps to ensure that the CCI data needs of the climate research community are being met.

The first task for CMUG was an extensive gathering of climate research community user requirements, alongside GCOS requirements, and measured against CCI specifications.  With CCI Phase 1 data now available the CMUG has started publishing the results of its independent analyses of these data. An assessment by CMUG of the CCI Phase 1 CDRs of the sea surface temperature, ocean colour, sea ice, sea level, soil moisture, land cover, fire, aerosol and ozone datasets will be presented. This will comprise not only looking at the primary variables of each ECV but also the associated uncertainty estimates necessary for climate modelling and reanalysis applications. Results showing consistency between ECVs from the integrated CMUG assessments and through assimilation experiments will also be shown.  The tools developed by CMUG for assessing climate models and data will also be demonstrated.

The means for serving CCI datasets to the climate research community will also be discussed as will the use of climate modelling, reanalyses and CCI data for proposed climate services.

Measurements of the vertical profile of atmospheric constituents are often obtained with the inversion of remote sensing observations. In this case the observations are made in domains (spectral frequency and/or parameters of the geometry of observation) that do not coincide with the domain (either altitude or pressure) used for the representation of the profile. Accordingly, the vertical grid, made of the discrete points used for this representation, is a choice arbitrarily made during the retrieval process.

We discuss the choice of the vertical grid of atmospheric profiles retrieved from remote sensing observations considering the two cases of profiles used to represent the results of individual measurements and of profiles used for subsequent data fusion applications.

We compare the individual retrieval results and the data fusion results when the profiles are retrieved using different vertical grids: selected according to the vertical resolution of either the individual profile or the fused profile. We perform this comparison using the ozone measurements of the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) instrument onboard the ENVISAT satellite. The observations of a limb sounding sequence of the instrument are divided into two complementary sets, made of observations at different limb angles, and two profiles are independently retrieved from the two sets of observations. The two profiles are retrieved a first time on different vertical grids each one corresponding to the tangent altitudes of the observations included in the set and a second time on a common vertical grid corresponding to the tangent altitudes of all the observations of the full sequence. Therefore, the first time the vertical grids are each optimized for the retrieval of the single set and the second time the common grid is optimized for the simultaneous retrieval and for the data fusion. In both cases the complete fusion algorithm is used for the fusion of the profiles. The results of the individual retrievals of the single sets and of the data fusions performed using the two different vertical grids are compared in terms of values, errors, vertical resolutions and number of degrees of freedom.

In the case of individual retrievals no evident advantage is obtained with the use of a grid finer than the one optimized for the individual retrieval. Nevertheless, this instrument dependent vertical grid, which seems to extract all the available information, provides very poor results when used for data fusion. A loss of about a quarter of the degrees of freedom is observed when the data fusion is made using the instrument dependent vertical grid relative to the data fusion made using a vertical grid optimized for the data fusion product. This result is explained by the analysis of the eigenvalues of the Fisher information matrix and leads to the conclusion that the vertical grids of products used for subsequent data fusion operations must be chosen taking into account the expected quality of the fused profiles, rather than using the choices made for the representation of the results of the individual retrievals.

Antarctica is an inhospitable empty continent. The only possible in-situ measurement networks are those for which robust methods and sensors exist that can withstand harsh conditions and run in fully automatic mode unattended over long periods of time. This is not the case for the measurement of precipitation. The measurement of snowfall is notoriously difficult in general, and difficulties are exacerbated in Antarctica. On the high Antarctic plateau, where less than 10 cm of equivalent water accumulate each year, frost deposition and extreme cold temperature adversely affect traditional precipitation gauges. At the peripheries, catabatic winds induce frequent blowing snow which blur snow fall observation. Databases of precipitation gauge reports such as that of the Global Precipitation Climatology Center (GPCC) are virtually void over Antarctica. A lack of validation data may be a reason why climate models used to predict climate change, such as those in the CMIP5 archive, considerably diverge as to their reconstruction of even the current continental-mean antarctic precipitation.

The models all predict that antarctic precipitation will increase in a warming climate, contributing a moderation of sea-level rise due to other causes. Unsurprisingly, the models diverge as to the magnitude of the increase. The drier models for present climate tend to predict a larger precipitation increase. There is thus a strong need for a model-free (i.e. not based on meteorological analyses or re-analyses) climatology of antarctic precipitation to evaluate and validate the models. The 1^st such product was recently assembled using CloudSat Cloud Profiling Radar (CPR) data. While other radars onboard satellites have been specifically designed to measure precipitation, none currently flies over the polar regions. CloudSat does. In the future, EarthCare will carry a similar radar with improved capabilities, and it will also fly over the polar regions. If properly calibrated and validated, the methods used to measure precipitation using CloudSat CPR data will be directly applicable to EarthCare data.

Consequently, The APRES3 (Antarctic Precipitation: REmote Sensing from Surface and Space) program was recently launched and will :

- acquired and process a set of observations in Antarctica to characterize antarctic snow fall (see partner presentation by Grazioli et al. in this conference)

- use the data to calibrate and validate CloudSat snow fall retrieval over Antarctica

- spatially and temporally expand the CloudSat climatology

- finally, based on the above, evaluate and improve meteorological and climate models (see partner presentation by Madeleine et al. In this conference) with respect to depicting and predicting antarctic precipitation.

Validation and progress made with CloudSat in this program will be timely for use with EarthCare data when finally in space and operational.

Atmospheric water vapor is a key factor in climate studies and weather forecasting. Different methods have been used to precisely determine its content in the atmosphere either from the ground or space. Microwave signals transmitted form space-borne sensors encounter a time delay when traversing the Earth’s neutral atmosphere. The delay in the observations from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) has been employed to determine the atmospheric water vapor content. Our results show a strong agreement between the estimated precipitable water vapor (PWV) and the reference data from, for example, the optical sensor MERIS.  

Numerical atmospheric models are increasingly used to provide simulations of the atmospheric parameters at high temporal and spatial resolutions. The accuracy of the models is still to be improved. Data fusion aims at integrating multiple data sources that can be redundant or complementary to produce complete, accurate information of the parameter of interest.

In this work, data fusion of PWV estimated from remote sensing observations, InSAR and GNSS, and data from the Weather Research and Forecasting (WRF) modeling system is applied to provide complete, accurate grids of PWV. Our goal is to infer spatially continuous, precise grids of PWV from heterogeneous data sets. This is done by a geostatistical data fusion approach based on the method of fixed-rank kriging. The first data set contains absolute maps of atmospheric PWV produced by combining observations from GNSS and InSAR. These PWV maps have a high spatial density and an accuracy of submillimeter; however, the data are missing in regions of low coherence (e.g., forests and vegetated areas). The PWV maps simulated by the WRF model represent the second data set.

The model maps are available for wide areas, but they have a coarse spatial resolution (3 km×3 km) and a yet limited accuracy. The results show that the PWV maps inferred by the data fusion at any spatial resolution are more accurate than those inferred from single data sets. The model data affect mainly the regions where the observations are missing. The spatial correlation coefficient with the reference MERIS data exceed 86% with RMS values below 1 mm.

Today the most advanced observations of the atmosphere, either from ground or space, are achieved by remote-sensing instrumentation. In particular lidars (optical active remote sensing instruments) are used to characterize aerosols and clouds. The level of detail depends on the complexity of the system, ranging from layer boundaries to microphysical parameters. One general problem is that, due to spectral limitations, inversion of optical data to retrieve the aerosol size distribution and complex refractive index is a mathematically ill-posed problem, and no single solution can be extracted. However, a qualitative classification based on measured intensive optical parameters  can be equally useful to distinguish between different aerosol types.

Our algorithm relies on artificial neural networks (ANN), which are trained to identify the most probable aerosol type from multiwavelength Raman depolarization lidar data. An ANN represents a mathematical projection of the human neural network. It is based on neurons, axons and synapses, the information being propagated as a neural influx. The ANN unit consists of neurons at each layer’s base, which include input, output and hidden layers. The outputs of the first layer become the input to the next layer.

Eight intensive optical parameters can be computed from the standard product of EARLINET’s multiwavelength Raman depolarization lidars:  two lidar ratios, one Angstroem exponent, one linear particle depolarization, two color ratios and two color indexes. These are considered inputs for the ANN in our algorithm. Fourteen aerosol types were defined based on their composition, and a synthetic database was generated to produce a comprehensive set of cases to train the ANN: pure types (continental, continental polluted, dust, marine, smoke, volcanic), mixed types (continental dust, marine mineral, continental smoke, dust polluted, coastal, coastal polluted, mixed dust, mixed smoke).

Forty-eight ANN structures were tested, out of which three were selected to make the classification, based on their performances: Jordan/Elman with 6 and 8 hidden layers respectively, and Generalized feedforward with 6 hidden layers. Supervised training with Momentum or Conjugate gradient learning rule was applied. Two levels of resolution have been considered, providing: b) detailed classification from fourteen aerosol types (six 90% pure, six mixtures of two types, two mixtures of three); b) rough classification from six aerosol types (min. 70% pure and 30% residuals). High resolution typing is proper for advanced lidar systems which provide highly accurate optical data (less than 10% uncertainty). Low resolution typing can still be performed on optical data with an uncertainty less than 20%.

The algorithm was tested in three steps. The aerosol model used to train the ANNs was compared to the literature and with observations, specifically with the ESA EARLINET-Calipso database. Each ANN was tested individually, and performances after the training were assessed to be within the requirements (at least 10 aerosol types recognized, out of 14 defined;  more than 90% of the cases pass the discrimination levels: 75% for pure aerosols and 65% for mixed aerosols). Blind tests using synthetic profiles in various atmospheric scenarios were applied to identify the sensitivity of the retrieval with respect to data uncertainty, aerosol mixing, aerosol load, and humidity. Less than 9% of the cases were missed or improperly classified by the high resolution ANNs, and less than 6% by the low resolution ANNs. Further tests on observations from different EARLINET stations will be performed within the next months. The applicability of a similar classification procedure to satellite lidars is still to be assessed. Considering the limited number of physically strong optical parameters provided by Calipso, as well as by ADM-Aeolus and EarthCARE in the future, only low resolution typing is foreseen.

A decade’s worth of trace gas and aerosol observations from the Ozone Monitoring Instrument (OMI), on-board NASA’s Aura satellite, is used to study changes in local air-quality at over 500 locations across the Persian Gulf and Middle East.

A detailed time series analysis is performed to examine the temporal variations in nitrogen dioxide (NO₂), formaldehyde (HCHO), sulphur dioxide (SO₂), and retrieved aerosol parameters, as observed by the OMI instrument over 2005-2014. The variations in these trace gases within about 10 km of over 500 locations in the Persian Gulf regions are quantified.  Statistical tools based on those used for greenhouse gas analyses, are used to determine the overall trend and growth rate variability, and seasonal variations in these air-quality metrics. The variability of ancillary retrieval parameters such as air mass factors, cloud cover and aerosol loading are also examined to determine if observed changes in these trace gases are in fact real.

The analysis shows that air-quality at over 70% of locations within the Persian Gulf is significantly getting worse and that regulatory control over surface pollutant emissions is drastically need to mitigate the subsequent impact on air-health.

The tools and analysis presented will provide valuable guidance in how to interpret air-quality data from future missions (e.g. TROPOMI on-board ESA's Sentinel-5 satellite).

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As part of ESA's climate change initiative   high vertical
resolution ozone profiles from three instruments all aboard ESA's Envisat (GOMOS, MIPAS, SCIAMACHY) in combination
with ESA's third party missions (OSIRIS, SMR, ACE-FTS)  and U.S. Sensors (MLS, HALOE, SAGE II, SABER) are to be combined in order to create
an essential climate variable data record. A~prerequisite before combining data
is the examination of differences and drifts between the datasets. In this paper, we present a~detailed
analysis of ozone profile differences based on pairwise collocated measurements, including the evolution
of the differences with time. Such a~diagnosis is helpful to identify strengths and weaknesses
of each  data set that may vary in time and introduce uncertainties in long-term trend estimates.
The analysis for the first six sensors reveals that
the relative drift between the sensors is not statistically significant for most pairs of instruments.
The relative drift values can be used to estimate the added uncertainty
in physical trends. The added drift uncertainty is estimated at about 3% per decade (1 sigma).
Larger differences and variability in the differences
are found in the  lowermost stratosphere (below 20 km)  and in the mesosphere. In the second step the evaluationof  all combinations of 10 sensors are presented with their pairwise drift and bias analyses.

Aerosols are an important regulator of the Earth’s climate. They scatter and absorb incoming solar radiation and thus cool the climate by reducing the amount of energy reaching the atmospheric layers and the surface below (direct effect). A certain subset of the particles can also act as initial formation sites for cloud droplets and thereby modify the microphysics, dynamics, radiative properties and lifetime of clouds (indirect effects). The magnitude of aerosol radiative effects remains the single largest uncertainty in current estimates of anthropogenic radiative forcing. One of the key quantities needed for accurate estimates of anthropogenic radiative forcing is an accurate estimate of the radiative effects from natural unperturbed aerosol. The dominant source of natural aerosols over Earth’s vast forested regions are biogenic volatile organic compounds (BVOC) which, following oxidation in the atmosphere, can condense onto aerosol particles to form secondary organic aerosol (SOA) and significantly modify the particles’ properties. In accordance with the expected positive temperature dependence of BVOC emissions, several previous studies have shown that some aerosol properties, such as mass concentration and ability to act as cloud condensation nuclei (CCN), also correlate positively with temperature at many forested sites. There is conflicting evidence as to whether the aerosol direct effects have temperature dependence due to increased BVOC emissions. The main objective of this study is to investigate the causes of the observed effect of increasing temperatures on the aerosol direct radiative effect, and to provide a quantitative estimate of this effect and of the resulting negative feedback in a warming climate. More specifically, we investigate the causes of the positive correlation between aerosol optical depth (AOD) and land surface temperature (LST) over boreal forests where biogenic emissions are a significant source of atmospheric particles. In addition to BVOCs, SOA formed in aqueous phase and biomass burning emissions could explain the temperature dependence of aerosol direct radiative effect. The study is done using a combination of satellite data and climate modeling. Key remote sensing data used are the aerosol optical depth and land surface temperature AATSR products available from the Aerosol-CCI and GlobTemperature projects, together with ancillary data, such as column concentrations of CO and water vapour from AIRS, NO₂ from OMI, and aerosol profiles from CALIOP, and ESA's Soil Moisture-CCI products. The aerosol-chemistry climate model used is ECHAM-HAMMOZ, which describes all known relevant atmospheric aerosol processes. It includes all the main atmospheric aerosol compounds as well as the interactive biogenic emission model MEGAN, which enables the simulation of the effects of temperature changes on atmospheric aerosol load. With these tools, we estimate the significance of the negative feedback due to a warming-induced aerosol direct effect and specify the aerosol species contributing to it.

In support of the ESA EO Sensor Performance, Products and Algorithm activities (https://earth.esa.int/web/sppa/home), we propose a new approach for analyzing the effects of small-scale structures on the GOMOS (Global Ozone Monitoring by Occultation of Stars) High Resolution Temperature Profiles (HRTP).

HRTP are derived from measurements with two fast photometers whose signal is sampled at 1 kHz, and allowing to investigate the role of irregularities in the density and temperature profiles, such as those associated with gravity waves.

In this study high resolution temperature and density profiles measured at high latitude by GOMOS are compared with observations made with the ground-based aerosol/temperature lidar at Thule (76.5°N, 68.8°W), Greenland.  The lidar at Thule contributes to the Network for the Detection of Atmospheric Composition Change.  The lidar profiles are analyzed in the height interval overlapping with GOMOS data (18-37 km), and the density and temperature profiles are obtained with 250 m vertical resolution.  The comparison is focused on data collected during the 2008-2009 and 2009-2010 Arctic winters, and by selecting profiles measured within about 2 hours and about 300 km.  A relatively large number of overpasses is found at high latitude, allowing the comparison of high vertical resolution structures in the night-time polar stratosphere. 

Several corresponding signatures can be identified in the GOMOS and lidar profiles, suggesting that the GOMOS HRTP could be used to investigate the global distribution of gravity waves.  The results of temperature and density profiles validation will be discussed in detail, along with some specific cases of gravity waves.

The comparison is also made using the continuous wavelet transform technique (Iannone et al., 2014), which allows the unambiguous detection of local properties of a signal across different scales; the same method is also used to remove non-stationary phenomena from the retrieved temperature profile, with the aim of minimising the impact of atmospheric fluctuations in the framework of validation exercises.

 

References

Iannone R. Q., S. Casadio, and B. R. Bojkov, 2014: A new method for the validation of the GOMOS High Resolution Temperature Profiles products. Annals of Geophysics, doi: 10.4401/ag-6487.

 

Environment Canada (EC) is commissioning a meteorological supersite in Iqaluit (64^oN, 69^oW) with the objective of designing an observing system for the Canadian Arctic. The site is uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites such as ADM-Aeolus, EarthCare, and GPM, making it useful for satellite calibration/validation (cal/val).

As highlighted during the 2015 ADM-Aeolus cal/val meeting, there is a pressing need for ground-based cal/val sites in the Arctic. The Iqaluit supersite was designed to address this need. Several instruments including two scanning Doppler LiDARs, a Ka-band radar, an X-band radar, radiometers, scintillometers, a 449 MHz wind profiler, an aerosol LiDAR, and a ceilometer will complement the currently available surface and radiosonde meteorological measurements. This set of instruments will provide near-real time observations of altitude resolved wind speed & direction, cloud properties, sensible heat flux, turbulence, water vapour, etc., in the troposphere.

This presentation will describe the Iqaluit supersite and present initial results to demonstrate its capabilities. The proposed EC methodology for ongoing cal/val of the ADM-Aeolus mission will be discussed. One of the objectives of this presentation will be to open dialogue for Arctic collaboration around an optimal observing system for this region which will merge ground-based observations, satellite data, and numerical weather forecast models.

The presentation discusses the comprehensive assessment of sensitivity tendencies in characterization of atmospheric aerosol from remote sensing. These sensitivities were identified in series of numerical tests using the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm (Dubovik et al. 2014). GRASP is a highly versatile algorithm that can applied to a variety of remote sensing observations or any of their combination. Moreover, GRASP is highly flexible and can be applied for inverting the same observations with various assumption in both forward calculations and numerical inversion. Therefore, GRASP is very convenient tool both for modeling remote sensing signals for different scenarios of observations and for testing the possibilities of retrieving properties of aerosol and surface from those observations.

Here, we propose a unique series of sensitivity tests, that are not focused on a particular case or a particular instrument, but rather aimed on establishing some guidelines for understanding information content limitations and providing recommendation on the retrieval optimizations. The study attempts to provide responses different questions that have been brought up in the scope of the aerosol remote sensing retrieval activities during the last past years. For example, we analyze the effects of various algorithmic assumptions on the retrieval results, such as assumptions on number of aerosol modes and assumption of specific shapes of aerosol size distributions, etc. Also we analyze possibilities to distinguish optical properties of each aerosol modes of aerosol mixture, possible to separate the optical properties from a hypothetical aerosol mixture and in which conditions, the importance of multi-angular and polarimetric observations for achieving improvements in the retrievals, etc.

[*] The work is performed within the framework of the program IDEAS+ supported by ESA 

Reference:

[1] Dubovik, O., T. Lapyonok, P. Litvinov, M. Herman, D. Fuertes, F. Ducos, A. Lopatin, A. Chaikovsky, B. Torres, Y. Derimian, X. Huang, M. Aspetsberger, and C. Federspiel “GRASP: a versatile algorithm for characterizing the atmosphere”, SPIE: Newsroom, DOI:10.1117/2.1201408.005558, Published Online: September 19, 2014. http://spie.org/x109993.xml .

http://evdc.nilu.no is a data infrastructure hosted, operated and maintained by NILU-Norwegian Institute for Air Research, on behalf of ESA. EVDC is the official ESA repository for the validation and campaign datasets, and provides the final archive for the data.  EVDC currently store more than 360 000 individual data sets from a wide range of measurement platforms such as aircraft, balloon, groundbased and satellite. 

EVDC uses the Generic Earth Observation Metadata Standard (GEOMS), which offers a set of generic metadata guidelines for Earth Observation data, to facilitate exchange of validation data among investigators and missions. GEOMS is a common effort between ESA, NASA, NDACC and related universities and organizations. 

Aiming to expand the validation datasets and to interconnect several data-centres, the DCIO initiative (Data Centre Inter Operability) has been defined in EVDC. By connecting datacentres and by exchanging catalogue metadata, users will have one interface to discover potential datasets and, if available, direct and free access could be offered. 

A large fraction of the Sentinel-5P and ADM-Aeolus validation data shall be stored in EVDC. 

In collaboration with ECMWF, EVDC provides access to daily updated analyses and forecast data files of global gridded meteorological parameters, such as temperature, geopotential height, wind,  total ozone maps, etc. Analysis and forecast up to 240 hours for all levels from ground and up to the top of the atmosphere are updated on a daily basis, and data for both the Northern and Southern hemisphere are available. 

Aiming to supporting the inter-calibration and the intercomparison of ESA heritage and current missions, tools for generating overpass subsets of data will be set up and operated to facilitate the online access to Cal/Val datasets.

EVDC provides first line support for scientists using or providing data to the database, both
on regular basis and intensive campaign phases. Specific campaign web pages are made
available on request. 

We present a new method for the derivation of anthropogenic emission estimates for aerosols, CO and SO₂. The method applies a novel approach using the observed relationships between measured satellite tropospheric columnar levels of aerosols, CO, SO₂ and NOx in each grid box at low wind speeds, and the DESCO v1 NOx monthly emission estimates. The method is applied over China at a 1 deg X 1 deg grid, and emission estimates are derived for different seasons and years (thus emission trends are also calculated). The emission estimates thus derived appear to be consistent, both in their spatial distribution and their magnitude, and correlate well with existing inventories. The method has been developed and applied in the frame of the EU MarcoPolo project (Grand Number 606953, Theme SPA.2013.3.2-01).

One of the most important environmental issues in southern cities of Iran is Dust storm. It severely threatens the mankind society. This phenomenon may also affect the fertility of agricultural soils. Similar to all materials, dust also reflects the incident energy received from a given source. In other words, dust can affect the energy reflected toward sensors/satellites. Hence, a mass of dust directly influences the intensity of the energy. This study shows a high correlation between the dust density and the reflected energy recorded by satellites for each ground pixel. This research proposes an effective algorithm based on the linear spectral unmixing, which deploys the MODIS and Landsat images. The proposed method uses changes in the reflectance of particular pixels having invariant covers during the days the satellite images are acquired. These changes help model the dust density in terms of the dust aerosols abundance. It is obvious that the abundance values demonstrate the effect of the dust density on the reflectance of each ground pixel. The first part of the results shows the contribution of dust to the pixel reflectance. A comparison with the field-measured dust densities shows an R² of 0.90 between the measured densities and the dust reflectance abundances in each pixel (estimated from the linear unmixing). To evaluate the proposed method, it was applied to a new set of samples where an RMSE of about 1.34 (microgram / m^3) between the model-predicted and the field-measured dust densities was achieved. It is expected that this model can perform better for higher values of dust densities.

The ESA project „SEOM-Improved Atmospheric Spectroscopy Databases (IAS)“ will improve the spectroscopic database for retrieval of the data products CO, CH₄, O₃ and SO₂ column amounts measured by the TROPOMI instrument (TROPOspheric Monitoring Instrument) aboard the Sentinel-5 Precursor. The project was launched in February 2014 with 3 years duration.  The spectroscopy of CO, CH₄ and O₃ in the 2.3 µm region is covered in the first 2 years, while UV measurements of SO₂ and UV/FIR/IR measurements of ozone will be carried out in the last year. User requirements for the spectroscopic database were obtained by retrieval simulations, indicating the need to take line mixing into account in case of CH₄, to measure HDO in case of water and even to measure line broadening of CH₄ lines by water vapor. A dedicated line model is used to represent the laboratory spectra since the Voigt routine was found to be not sufficient to obtain the needed accuracy.  The measurements in the 2.3 µm region are finalized. The analysis is planned to be finalized by April 2016.. The new spectroscopic data will be presented and discussed.

Within ESA’s Earth Observation Envelope Programme, two candidate missions, called FLEX and CarbonSat, have been evaluated. Recently, the Earth Science Advisory Council has recommended FLEX for implementation as ESA’s eighth Earth Explorer and will be dealt with in a separate presentation.

CarbonSat aims to deliver global data sets of dry column mixing ratios of CO₂ and CH₄ with high precision (CO₂ < 1 ppm, CH₄ < 10 ppb for a single ground scene observation) and accuracy. A high spatial resolution (2 x 3 km²) and a broad swath (180–240 km) will allow global imaging of localized strong emission sources and enables the separation of anthropogenic from natural sources and sinks. CarbonSat’s coverage will provide a more than one order of magnitude larger number of cloud free measurements than any of the current generation greenhouse gas instruments on GOSAT and OCO. Spectral absorptions of CO₂ in the 1.6 μm and 2 μm bands, O₂ in the 760 nm and CH₄ in the 1.65 μm spectral ranges will be measured with high spectral resolution and a high signal-to-noise ratio. In this paper an overview will be presented of recent activities related to CarbonSat and an overview will be given of potential future activities.

The ATSR Dual View (ADV) and ATSR Single View (ASV) aerosol retrieval algorithms have been developed for use with the European Space Agency (ESA) Along Track Scanning Radiometers: ATSR-2 (1995-2003) and AATSR (2002-2012). The ATSR instruments provide the radiances at the TOA (top of the atmosphere) in 7 wavebands from the visible (VIS) to the thermal infrared (TIR) for two views: near-nadir and at 55 degrees forward. The dual view capability allows for elimination of the surface reflectance by using the k-ratio, which is the ratio of measured reflectance in the forward and nadir views in the 1.6 µm band, assuming that the effect of aerosols on the TOA reflectance is small at this wavelength.

The basic principle of the aerosol retrieval is to match ATSR measured top of atmosphere (TOA) reflectance to modelled reflectance. The modelled reflectance is computed by applying a radiation transfer code for the solar radiance through the atmosphere. In the radiation transfer equation a model for the local aerosol is needed. Cloud screening is needed as the aerosol properties can be retrieved only for cloud-free sky. The (A)ATSR wide spectral range allows for effective cloud screening. However, not all cloud pixels are rejected with the cloud screening currently applied in ADV/ASV and additional cloud post-processing is needed.

For each AOD-retrieved pixel, two tests were applied in the past to determine and discard the pixels that might potentially include cloud edges or residual sub-pixel clouds. Each pixel was analyzed together with the eight surrounding pixels in a 0.3° × 0.3° area. If, in addition to the tested pixel, less than three pixels were retrieved in the area, the tested pixel was considered as a cloud edge and discarded. If, besides the tested pixel, at least three more pixels were retrieved and the standard deviation of the AOD in the area was larger than 0.1, the tested pixel was discarded.

The value of the standard deviation equal to 0.1 has been introduced in previous version of the cloud post-processing as a compromise between global coverage and acceptable validation results. It has been used for producing AOD global datasets in the Aerosol_cci ESA project. However, for certain areas with high AOD (e.g. India, China), and for case studies of natural high AOD episodes (e.g. dust storms, volcanic eruptions) different values need to be used to obtain credible results.

The histogram approach is introduced in current study to recognize the high AOD loading areas. We present the results for cloud post-processing thresholds for high and low AOD loading episodes and show the influence of the thresholds chosen on AOD mean values and AOD time series.

 

ACKNOWLEDGEMENTS

 

Work presented is supported by the ESA-ESRIN projects Aerosol_cci Phases 1&2 (project AO/1-6207/09/I-LG), MarcoPolo (EU FP7 Grant agreement no 606953) and BACCHUS (EU-FP7 Grant agreement no 603445.

The MIPAS mission is in its post-operational phase F. The data set covers a period of ten years from 2002 to 2012 and has an excellent proven quality. The MIPAS instrument has measured Infra-Red (IR) emission spectra at the Earth's limb in the middle and upper atmosphere along the orbit during day and night. The spectral range covered is 685 to 2410 cm-1 with a spectral sampling of 0.025 cm-1 during Full Resolution (FR) period and 0.0625 cm-1 during Optimised Resolution (OR) period.

The MIPAS level 1B processor transforms instrument raw data into spectra calibrated spectrally and radiometrically with geo-location determined over the earth geoid of the line of sight tangent point.

This paper describes areas for improvements of algorithms and calibration to increase data quality further before the last regeneration of the full Level 1B MIPAS data sets and final archiving. An assessment of data quality will be given in term of NESR, radiometric accuracy, spectral accuracy and pointing knowledge.

Sentinel-5 Precursor (S5P) is the first atmospheric composition satellite within the European Copernicus Programme. With its single payload TROPOMI (TROPOspheric Monitoring Instrument), a nadir-viewing spectrometer covering spectral channels in the UV, visible, near- and short-wave infrared, the mission will operationally provide key atmospheric composition products. The mission will be operated in formation with NOAA’s Suomi-NPP spacecraft, improving the S5P operational products and allowing synergistic use of both missions.

The primary S5P objective is to provide space-borne information for the Copernicus Atmosphere Monitoring (CAMS) and the Climate Change Service (C3S). In addition, a wide range of scientific topics related to atmospheric processes related to Air Quality, stratospheric ozone and climate forcing will benefit from S5P.

During the exploitation phase (Phase E2) of the S5P mission a number of tasks are required, in order to ensure that the Copernicus Services receive the   S5P products of the expected quality. These activities are coordinated by the S5P Mission Manager and cover the Payload Data Ground Segment (PDGS), the Flight Operations Segment (FOS), the technical Post Launch Support, the support to data exploitation as well as S5P outreach activities.

The S5P Mission Performance Centre (MPC), as integral part of the PDGS, is the core ESA quality assurance element for products during the exploitation phase of the mission. It will be responsible for the operational Level 1b and 2 product quality control, the long term monitoring of the instrument performance, the inflight calibration and characterisation of the payload, the basic validation of the products using independent datasets, as well as the maintenance and evolution of the processing algorithms. This will be achieved by establishing a team of experts providing services and expertise related to calibration/validation, near-real time/off-line quality control and algorithms corrective and perfective maintenance.

The paper will present the overall Phase E2 planning for the S5P mission as well as the S5P MPC.

Aerosols in the stratosphere affect crucially not only the radiative budget of the Earth by changing the way radiation is transmitted through the atmosphere, but also play an important role in the chemical processes which lead to the ozone layer depletion. It is well known, that the background stratospheric aerosol composition is determined by the tropical injections of SO₂, COS and sulfate particles from the troposphere, but this background composition is occasionally perturbed by vast SO₂ emissions, caused by very large volcanic eruptions. Most commonly stratospheric aerosol is characterized by a particle size distribution and particle number density. A reliable knowledge on these parameters is important among other for various modeling activities related to stratospheric processes. Measurements of the transmitted or scattered solar light in visible and near infrared spectral region performed by satellite instruments in occultation and limb viewing geometry, serve as one of the most important sources of global information on the spatial and vertical distribution of the stratospheric aerosol characteristics. In this research we use limb measurements from space borne spectrometer SCIAMACHY, operated on board the ENVISAT satellite from August 2002 to April 2012. We present a retrieval method, sensitivity studies and first results on the aerosol particle size distribution parameters as well as aerosol particle number density retrieved from SCIAMACHY limb measurements.

The AIRWAVE (Advanced Infra-Red WAter Vapour Estimator) algorithm has been developed for the retrieval of TCWV from the measurements of the Along Track Scanning Radiometer (ATSR) missions. The first version of the algorithm makes use of the TIR channels of ATSR-like instruments, exploiting the dual viewing geometries to infer the TCWV over the sea. When applied to the whole ATSR missions (ATSR-1, ATSR-2 and AATSR) it produced TCVW in good agreement with the results obtained by the Special Sensor Microwave/Imager (SSM/I) and radiosondes.

The algorithm makes use of a set of tabulated parameters. In the first version of the algorithm, these parameters were fixed along the whole globe, and were calculated as a weighted average of the ones for Tropical and Mid-Latitude scenarios. We have updated the algorithm with a new set of parameters that improve the performances at all latitudes. The new sets of parameters were calculated accounting for the four seasons and atmospheric state (e.g. Tropical, Mid-Latitude and Polar conditions in January, April, July and October). We computed the new set of parameters with a Radiative Transfer forward Model (RTM) that was specifically developed to simulate ATSR radiances. We have also exploited the RTM  to evaluate the impact of atmospheric and surface conditions (e.g.  atmospheric temperature and water vapour profiles, HNO3, CO2, CFCs amount, Sea Surface Temperature (SST)) and instrument viewing angles on the calculated parameters.  

Since each of the simulated conditions produce a different set of retrieval parameters, we had to solve how automatically chose the most suitable ones to be used in each case. The choice is now performed through the use of a Neural Network (NN) that was trained using the above mentioned parameters over a set of radiances simulated with the RTM code and ECMWF temperature, pressure and water vapour profiles.

Here we present the results of the application of the updated AIRWAVE algorithm to a subset of ATSR data and evaluate the performances with respect to the ones of the original version.   

The World Health Organization and the European Environment Agency classifies air pollution as the top environmental cause for premature deaths with utmost impact on productivity and wellbeing. Although major relevance have been given by legislators with restrictive regulations is a well-known fact that citizens often breathe air that does not meet quality standards. Chemicals like ozone, nitric oxides, carbon monoxides, and particulate matters are jeopardizing human health. According to WHO, airborne diseases affects circa 25% of world population ranging from the ones affecting the respiratory system (asthma, chronic obstructive pulmonary, lung cancer) to allergic rhinitis, allergic conjunctivitis and dermatologic disorders.  

Risks may be minimized avoiding exposure to air pollution and SOUL (Sensor Observation of Urban Life) solution becomes a tool for proactive decisions. Aggregating data from near-real time sensors network installed in moving vehicles that mapps dynamically the city, with Earth Observation missions satellites images, personalized information, tailored by medical profile, is given to the individuals regarding air quality. These air-quality indicators are also developed correlating the measurements with bio parameters in order to allow the user to only receive alerts from health conditions of his relevance. Machine learning algorithms are under development for the implementation of air quality forecast. SOUL is a technological scalable solution, providing accurate real-time alerts in web and mobile platforms, being incubated at ESA BIC Portugal and MIT/BGI, and financed by the European Commision.

We present the results of the study aiming on deriving enhanced aerosol and surface properties from Envisat/MERIS and potentially Sentinel-3/OLCI observations using recently developed GRASP algorithm (Generalized Retrieval of Aerosol and Surface Properties) algorithm described by Dubovik et al. (2014).  GRASP derives extended set of atmospheric parameters using multi-pixel concept - a simultaneous fitting of a large group of pixels under additional a priori constraints limiting the time variability of surface properties and spatial variability of aerosol properties. Over land GRASP simultaneously retrieves properties of both aerosol and underlying surface even over bright surfaces. GRAPS doesn’t use traditional look-up-tables and performs retrieval as search in continuous space of solution. All radiative transfer calculations are performed as part of the retrieval.

The results of comprehensive sensitivity tests, as well as results obtained from real Envisat/MERIS data will be presented. The tests analyze the accuracy of the retrieval of various aerosol and surface reflectance parameters at different spatial resolutions.  Both the results of numerical tests, as well as results obtained from Envisat/MERIS data illustrate demonstrate reliable retrieval of AOD (Aerosol Optical Depth) and surface BRDF. Moreover, for some situations we illustrate possibilities of retrieving aerosol absorption – property that hardly accessible from satellite observations with no multi-angular and polarimetric capabilities.

 

(*) The study has been supported by ESA within framework of CAWA project.

Dubovik, O., T. Lapyonok, P. Litvinov, M. Herman, D. Fuertes, F. Ducos, A. Lopatin, A. Chaikovsky, B. Torres, Y. Derimian, X. Huang, M. Aspetsberger, and C. Federspiel “GRASP: a versatile algorithm for characterizing the atmosphere”, SPIE: Newsroom, DOI:10.1117/2.1201408.005558, Published Online: September 19,  2014. http://spie.org/x109993.xml .

Recent studies have shown that neglecting the horizontal variability of the atmosphere in the forward model for the simulation of limb emission radiances causes a systematic error in MIPAS retrieved profiles. While not visible in the individual profiles, this systematic effect is noticeable as a difference between zonal averages obtained from the ascending and descending parts of the satellite orbit. To improve the quality of MIPAS products, it is desirable to take into account the horizontal variability of the atmosphere in the forward model. There are several methods to account for the atmospheric horizontal variability, one of them is to introduce an altitude dependent horizontal gradient for the quantities used to describe the atmospheric state.

The introduction of the horizontal gradient model into the Optimized Retrieval Model (ORM), the scientific prototype of the processor used by ESA to obtain MIPAS level 2 products, requires a new design of the code itself. Several optimizations exploiting the spherical symmetry of the atmosphere can no longer be used. Therefore, both the ray tracing and the radiative transfer integration algorithms need to be adapted.

In this work we illustrate the choices adopted for the implementation of the horizontal gradient model, and show the self-consistency of the newly implemented retrieval scheme. We show its performances versus the previous algorithm that assumes the horizontal homogeneity of the atmosphere. Finally we compare the new forward model with codes that take into account the horizontal variability of the atmosphere (like GMTR and KOPRA).

Satellite observations in limb or occultation geometry provide height resolved information about the atmospheric state. A critical point here is the pointing knowledge, i.e. the precise knowledge of the viewing direction which determines the observed tangent height.

The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT (2002-2012) performed nadir, limb, solar/lunar occultation and various monitoring measurements. The pointing information of the instrument is determined by the attitude information of the ENVISAT platform and its star tracker together with the encoder readouts of both the azimuth and elevation scanner of SCIAMACHY.

In this work, we investigate the pointing performance of the instrument and derive improved mispointing parameters. SCIAMACHY's solar and lunar measurement are used to find the viewing direction towards sun and moon, respectively. The solar measurements over the limb port (solar occultation) and over the sub-solar port allow the measurement of the position of the center of the solar disk, because the sun has a rotational symmetry.

The lunar measurements over the limb port (lunar occultation) have a more difficult target. The lunar disk is highly variable, varying with lunar phase and libration. SCIAMACHY's sun follower device (SFD) is used to lock the viewing direction to the moon and adjusts towards the gravity center of intensity of the lunar disk. We simulated the behavior of the SFD with 15 reference images from the USGS Robotic Lunar Observatory (ROLO). We developed a parameterization for the location of the intensity center depending on the observation geometry, so that we can utilize all lunar measurements close to full moon for the pointing investigations.

The three types of measurements are combined to improve the pointing knowledge. In a global fit, the pointing offsets are minimized by varying the mispointing parameters. The improved mispointing parameters will be used for the next version of the level 0-1b processing of SCIAMACHY. All retrieved products from SCIAMACHY's limb and occultation measurements will benefit. This work is embedded in the SCIAMACHY Quality Working Group.

A. Laeng¹, T. von Clarmann¹,  G. Stiller¹, U. Grabowski¹, N. Glatthor¹, S. Lossow¹, S. Kellmann¹, M. Kiefer¹, A. Linden¹, N. Kramarova², J. Zawodny³, S. Godin-Beekmann⁴, I. Petropavlovskih⁵, R. Stubi¹⁰, G.Ancellet⁴, E. Maillard-Barass¹⁰, T. Leblanc⁶,  W. Steinbrecht⁷, T. Portafaix⁸, A. v. Gijsel⁹

 

¹KIT IMK-ASF, Karlsruhe, Germany

²NASA SSAI, USA

³NASA Langley Research Center, Hampton, USA

⁴LATMOS, IPSL, France

⁵NOAA, Boulder, CO, USA

⁶NASA JPL California Institute of Technology, USA

⁷Deutscher Wetterdienst, Offenbach, Germany

⁸LACy, France

⁹RIVM,  Netherlands

¹⁰MeteoSwiss, Switzerland

 

 

MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) on board the ESA ENVISAT spacecraft has been taking limb emission measurements of ozone profiles from 2002 to April 2012.The Stratospheric Aerosol and Gas Experiment II (SAGE II) took solar occultation measurements of ozone number densities from 1984–2005 and has been used in many studies of long-term ozone trends.  The Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) instrument, launched in October 2011 and currently operated, measures solar radiances scattered from atmospheric limb in UV and visible spectral ranges to retrieve vertical ozone profiles from cloud top to 60 km with vertical resolution of about 2 km.

This information is used to merge the three ozone records, SAGE II, MIPAS and OMPS, into a single ozone record from 1984 to the present. First, the overall agreement of MIPAS with SAGE v7.0 and OMPS v2.0 and biases between datasets are investigated.  Vertical profiles from ozonesondes and Umkehr are used as transfer standard instruments. Then, ozone linear trends are derived by multivariate regression from obtained 30-years long ozone record. The comparison with trends from two of three parent datasets and the standard transfer instrument will be discussed.  The comparison with trends from previously merged SAGE II + OSIRIS and SAGE II + GOMOS datasets will be presented as well. Finally, the trends obtained will be compared with trends calculated on satellite overpasses over ground ozonesonde stations. The impacts of:

-    the choice of the transfer instrument

-    the way the standard is generated

-    the effect of neglecting any longitudinal structure in the transfer standard samples

 on ozone trends derived from the merged datasets will be discussed.

 

Aerosol Monitoring and Analysis of Winter Haze in North Eastern Pakistan

Badar Ghauri

Dept. of RS & GISc, Institute of Space Technology

Karachi, Pakistan

E-mail: b_ghauri@yahoo.com, badar.munir@ist.edu.pk

Cell: 0300-2731902

 

Abstract:

 

North eastern Pakistan is covered with dense haze during winters (Dec-Jan). This fog affects public health, agriculture, transport, trade and commerce activities during this period. Here we report ambient aerosol collection (PM2.5) and their chemical analysis from December 20 to January 15, 2014 at Lahore, Pakistan (31°32'N, 74°22'E). Considerably high concentrations of sulphate SO4 ^2- from 6 to 167 micro gram/m3, and nitrate NO^-3 from 8 to 92 micro gram/m3 were observed.  In parallel, trace element concentrations were also determined such as As 21 ng/m3, Sb115 ng/m3, Se 288 ng/m3, Zn which was exceeding the US EPA limits.  Crustal   elements such as Al, Mg, Mn, Ca, Fe, Cr, and Ni showed higher values per unit of air sampled during day compared to night time indicating soil entrainment caused by day time activities in the city. Source apportionment determined through Positive Matrix Factorization (PMF) revealed three main groups. The group I represented the soil related elements as Al, Mg, Mn, Ca, Fe,  Cr, .Group II  contained As , Sb, Se, Zn coming from vehicular and industrial emissions. The third group comprising sulphate SO4 ^2-and nitrate NO^-3 represented fossil fuel combustion in the city. Satellite data from MODIS (aerosol optical depth) were also acquired to get an insight of haze density and its extent. On the whole very high aerosol concentrations have been observed in this city during winters compared to other urban centers of the country.

 

Water vapour is the dominant greenhouse gas in the troposphere and has been increasing in the stratosphere as well. It is generally believed that stratospheric water vapor affects ozone chemistry in the stratosphere. HDO, one of the rare isotopologues, has been recently monitored by several operational satellite instruments by detecting thermal emission in the infrared and microwave range.

The balloon-borne TELIS (Terahertz and submillimeter Limb Sounder) instrument has been cooperatively developed by a consortium of European institutes, i.e. DLR, SRON, and RAL. Together with MIPAS-B and mini-DOAS operated by KIT and Heidelberg University, respectively, TELIS was installed on a stratospheric balloon gondola and has participated in four scientific campaigns since 2009. The high spectral resolution spectrometer TELIS allows the vertical information of the rare isotopologues between about 10 and 40 km by resolving power of individual lines. The concentration profile of HDO in the upper tropospheric and lower stratospheric can be observed by both the 1.8 THz (far infrared) channel and the 480--650 GHz (submillimeter) channel. For the far infrared frequency channel, the HDO product is retrieved from the 1818.50 GHz transition. We make use of the retrieval code PILS (Profile Inversion for Limb Sounding) to carry out the inversion and to assess the accuracy of the retrieval product.

In this work, we present the HDO retrievals from the 2009--2011 winter polar campaigns and compare the profiles with ones by other limb sounders, e.g. Odin/SMR, SCISAT/ACE-FTS, and Envisat/MIPAS. The outcome of this comparison helps us to better understand the measurement capabilities of the TELIS instrument and to make contribution to cross-validation of these spaceborne sensors.

An algorithm has been developed to retrieve total column of ozone (TCO) using MERIS data. Whereas total ozone has been usually measured from space for several decades using nadir UV backscatter sensors such as TOMS, SBUV, GOME, SCIAMACHY or OMI, we used here TOA reflectances spectrum in the MERIS spectral range [400 – 900 nm] which includes Chappuis bands .

Shortly, the method is based on the assumption that the TOA reflectance spectrum in the absence of gaseous absorption can be modelled by a third order polynomial. Excluding MERIS bands affected by water vapour and oxygen absorption, the free absorption spectrum can be derived using a 3^rd order polynomial fit on bands not affected by ozone (i.e.: 412.5, 442.5, 753.75, 778.75 and 865.0 nm). Difference between free absorption spectrum and measured spectrum is thus only due to ozone absorption. TCO is retrieved using method to minimize the chi-square difference between 3^rd order polynomial values weighted by ozone transmittance and measured reflectances.

This method has been applied to MERIS archive and is valid for bright and spectrally white surface such as snow/ice surfaces and over optically thick clouds: Antarctica is daily covered whereas coverage of others areas is limited. Over Antarctica, the MERIS TCO agrees reasonably with GOME-2A TCO product, although MERIS might lead to too low columns in case of ozone hole conditions. Outside of Antarctica, the MERIS – GOME-2A TCO differences are generally larger, and MERIS tends to under-estimate TCO.

First comparison between MERIS TCO and ground measurements shows a good correlation with a small bias of about 4 Dodson Units (DU) and an uncertainty (root mean square) of about 20 DU.

Climate change; forecasting its impact, monitoring the essential climate variables (ECVs) and mitigation strategies together with studies of the carbon and water cycles to provide policy makers with the unequivocal evidence of attribution and associated physical processes are today’s key societal challenges. These are identified as priorities not only in the ESA science strategy but those of other space agencies and international bodies alike such as World Meteorological Organisation (WMO), Group on Earth Observation (GEO).and increasingly the European Union and its Copernicus program.  This requires trustable, accurate, and multi-spectral data from satellite and in-situ observations over timescales sufficient to unequivocally detect small trends from the effects of natural variability i.e. decades.  Whilst the Sentinels of Copernicus and those of Eumetsat will provide key contributions to this data-set, they were primarily designed for more operational measurements and services.  The underpinning design and build accuracy was not specified in most cases to meet the exacting demands of climate, in common with most EO sensors, and yet we now look to them as the source of data upon which to build climate services and inform policy decisions. 

 

This limitation of the current Earth Observation system (in the context of climate) is recognised by scientists and space agencies and has led to ‘a strategy towards an architecture for climate monitoring from space’ [1].  One of the key elements within this strategy is the demand for a high accuracy SI traceable sensor(s) to provide a means to anchor and upgrade the performance of other EO sensors through in-flight reference calibration.  TRUTHS is a mission designed, amongst other things, to address this urgent and cross-cutting need, complementing the Sentinels and facilitating the establishment of a climate observatory in space and is highly complementary to its US sister CLARREO. For example, studies have been carried out to show how TRUTHS has the capacity to improve the radiometric calibration of sensors like Sentinel 2 & 3 to ~0.5% uncertainty levels.  It also directly addresses key science issues where its hyperspectral measurements of incoming and reflected solar radiation provide the diagnostic tools e.g. Radiation budget, Cloud radiative forcing, albedo, land cover/vegetation etc. In particular it provides a high accuracy benchmark of the state of the Earth’s climate (TOA radiance/reflectance) from which decadal change of key climate radiation feedbacks can be detected to enable rigorous and early testing/constraining of climate model forecasts.

 

Successful operational and commercially sustainable exploitation of knowledge from EO data, particularly in climate services is reliant on user confidence and trust in the underpinning data and its timely delivery from seamless combination of different data streams.  This fundamentally requires robust uncertainty estimates and bias removal through establishing traceability to international standards, at the highest level SI units. At present this is hard to achieve in the post-launch environment, particularly for optical sensors.

 

TRUTHS would provide measurements of incoming (total and spectrally resolved) and global reflected (320 -2350 nm at 5 nm FWHM) and spatially (50 m GIFOV) solar radiation at the 0.3% uncertainty level (0.01 % for total solar irradiance).  These fundamental climate data products (Level-1) can be convolved into the building blocks for many ECVs and EO applications.  Although not necessarily operationally delivering all of them itself it will ensure an integrated observing system, as envisaged by the 2015 ESA science strategy, can be cost effectively achieved.

 

The NPL led mission has been developed in conjunction with a wide consortium of partners, partly funded through grants from the UKSA through its CEOI-ST program.  Following strong scientific support for the mission by ESAC in its EE8 call the mission has undergone significant design optimisation to simplify and de-risk the payload, including prototyping and testing of key instruments and concepts, to alleviate early concerns on cost

 

[1] http://www.wmo.int/pages/prog/sat/documents/ARCH_strategy-climate-architecture-space.pdf

We present an innovative satellite-based UV (ultraviolet) dosimeter with a mobile App interface that has been validated in summer 2015 by exploiting both ground-based measurements and an in-vivo assessment of the erythemal effects on some volunteers having a controlled exposure to solar radiation.

The developed dosimeter is able to monitor in near real-time (15 minutes update frequency) the UV erythemal dose incident at ground by synergically exploiting the optical imagery provided by the SEVIRI sensor on the Meteosat Second Generation (MSG) geostationary satellite, the total columnar ozone density data provided by two different satellite sensors (i.e. GOME-2 on MetOp and OMI on AURA) and atmospheric radiative transfer modelling. The multi-resolution and multi-coverage source satellite data have been fused and a dedicated temporal-downscaling modelling has been developed in order to obtain UV erythemal dose data with a temporal resolution of 1 minute.

Satellite-based UV dosimetry has been coupled with a detailed dermatological modelling of skin response to UV radiation in order to provide a personalized maximum sun exposure time for avoiding erythemal effects or other possible skin diseases (such as melanoma). In particular a dedicated anamnestic questionnaire has been developed and integrated into the system in order to calculate the Minimal Erythema Dose (MED) of each user and provide a real-time alert when the maximum safe UV dose is reached. This model takes into account also the Solar Protection Factor (SPF) provided by possibly applied sunscreens.

This innovative system has been validated at Livorno (Italy) in a twofold way. First satellite-based UV dose data have been compared to ground-based UV radiation measurements. Secondly the maximum safe exposure time provided by the App has been compared to the actual time resulting from an in-vivo assessment performed by observing erythemal effects 24 hours after sun exposure. Ten volunteers wearing special UV-blocking T-shirts have been exposed to solar radiation in horizontal position: the T-shirts had 8 holes (4 with no sunscreen, other 4 with a sunscreen applied to the skin below) that have been covered in sequence at regulated time intervals. In this way we assessed the real safe sun exposure time (corresponding to the last hole with no erythemal effects after 24h) for comparison with the one provided by the App.

The results obtained have been successful in both cases. Indeed the statistical comparison between satellite-based UV dose data and the ground-based measured ones (6 months period) showed a good agreement:  a relative mean bias of 2 %, a relative RMSE of 25 %, a relative maximum absolute error (MAE) of 15 % and a correlation coefficient of 94 %.

Moreover for what concerns the in-vivo assessment, the maximum safe exposure times provided by the App would have guaranteed no erythemal effects to all volunteers, being equal or slightly lower with respect to the actually measured one.

The App has been lunched on the market in summer 2015 with the name of “HappySun” and it’s currently available both for Android and iOS devices.

Further R&D activities are on-going for performing a validation also in other countries (such as in the UK, with the collaboration of Public Health England) and for increasing further the dosimeter’s accuracy, e.g. by including other satellite-based inputs (such as aerosols optical depth) and by improving cloud properties modelling. The data that will be provided by Sentinel 4/5/5p satellites will be integrated into the system, most probably providing greater accuracy and higher temporal/spatial resolution.

CarbonSat is one of the two candidate missions selected for definition studies for becoming Earth Explorer 8 (EE8). The objective of the CarbonSat mission is to improve our knowledge on natural and anthropogenic sources and sinks of CO2 and CH4. The unique feature of the CarbonSat mission concept is its “Greenhouse gas (GHG) imaging capability”, which is achieved via a combination of high spatial resolution (3km X 2km) and a good spatial coverage. This capability enables global imaging of localized strong emission source areas such as cities, power plants, methane seeps, landfills and volcanoes, and allows better disentangling of natural and anthropogenic GHG sources and sinks.

CarbonSat entered industrial system feasibility activities (Phase A/B1) in 2012, which are supported by scientific studies and campaigns. In this context, an end‐to‐end mission performance simulation (E2ES) environment has been established to demonstrate mission maturity in phase A/B1 w.r.t. relevant S/W mission elements (relevant geometry characterization for nadir looking and glint modes, scene generator, instrument performance simulator, Level 0 to Level1 processing, Level 1 to Level 2 retrieval), to support mission and system performance consolidation and to provide a test bed for algorithm test, implementation and improvements in a later stage. After Phase A/B1 and mission selection, the E2ES environment can further evolve into a test bed for prototype and even operational processors.
 
The CarbonSat End-to-End Simulator (CSE2ES) simulates the full data flow of the mission with a set of modules embedded in ESA's generic simulation framework OpenSF. A Geometry Module (GM) defines the orbital geometry and related parameters. A Scene Generation Module (SGM) provides simulated radiances and irradiances for the selected scene. The Level 1 Module (L1M) compromises the instrument simulator and the Level 1b processor, and provide as main output calibrated spectra.  The L1M is implemented in two versions, reflecting the instrument concepts from the two competing industrial system studies. The Level 2 Retrieval Module (L2M) performs the retrieval from the input level 1b spectra to the atmospheric parameters (CO2 and CH4).

In this paper, we show sensitivity studies with respect to atmospheric parameters, simulations along the orbit and a case study for the detection of a point source emitting carbon dioxide. In summary, the end-to-end simulation proves the capability of the CarbonSat concept to reach its requirements.

In a constantly changing global atmospheric environment the need for continuous and reliable information on the levels of anthropogenic emissions is frequently required.  The latest improvements in satellite instrumentation as well as the advances in their measurement retrieval algorithms is now allowing us to extract these emission fields from space. Global ground measurements of air quality are often sparse and inaccessible, which make satellite observations the obvious tool to monitor country-wide pollution levels on a daily basis and to observe concentration trends on longer time scales. The GOME2 instrument on MetopA has been providing reliable information on the global atmospheric state since 2007 including total ozone, nitrogen dioxide, bromine oxide and sulphur dioxide among others. Evolving states and emerging markets are the obvious culprits for the increased levels of various trace gases and China is one of the main players of the past decade.

As part of EU FP7 Monitoring and Assessment of Regional air quality in China using space Observations, Project Of Long-term sino-european co-Operation, MarcoPolo, project, the long trends of atmospheric SO₂ have been studied from a spade of satellite instruments and algorithms. Point sources with negative trends have been identified, pointing to the implementation of air quality technologies, as well as locations with positive trends, pointing to the Chinese economy and industry increasing need for energy.

Using mathematical tools, as well as apriori SO₂ emission fields used in a renowned regional chemistry transport model, we investigate the possibility of improving the current emission fields by calculating a top-down emission inventory for China based on GOME2/MetopA SO₂ measurements.

For ten years (2002-2012), ESA’s Envisat satellite has provided an important contribution to the global measurement system for atmospheric composition. Three of its instruments, GOMOS, MIPAS, and SCIAMACHY, have measured the atmospheric abundance of a variety of trace gases and parameters, including reactive and greenhouse gases. The development of retrieval algorithms used for the derivation of atmospheric abundances from the Envisat-based measured spectra is in permanent evolution and continues during the current post-flight Phase F. This continuous evolution of Envisat data products requires appropriate independent data quality assessments and validations. In particular, the uncertainties and geophysical consistency of the Envisat data must be assessed for the wider range of atmospheric states and over the relevant spatial domain, vertical range, and mission lifetime. Every upgrade of the data products and associated data processors must be verified through delta-validation studies of the expected improvement. The outcome of delta-validation studies of successive data products furthermore provides highly valuable feedback to the respective data retrieval teams.

Pioneered in the nineties with the aim to study the mutual consistency between ESA’s ERS-2 GOME and NASA’s TOMS ozone monitoring instruments, a satellite validation system with multi-mission capacities has been developed at BIRA-IASB over the last two decades. This multi-mission QA/validation system for satellite atmospheric data, referred to as Multi-TASTE system, builds upon state-of-the-art, community-agreed validation protocols and practices implemented on a generic validation chain. Specific tools and methods include multi-dimensional observation operators for tailored co-location criteria, harmonised unit and representativeness conversions, information content analysers, and a range of statistical techniques to derive robust estimates of bias, spread, and long-term stability of the atmospheric data records. The validation chain includes comparison with reference data collected from established measurement networks contributing to WMO’s Global Atmosphere Watch (GAW), like the Network for the Detection of Atmospheric Composition Change (NDACC), and NASA’s Southern Hemisphere Additional Ozonesondes (SHADOZ) programme. The versatility of data products and retrieval approaches has been a key driver for the development of Multi-TASTE since the beginning. By now, global and long-term validation analyses are possible for both column and vertical profile data products of numerous reactive gases, greenhouse gases and temperature. The system is currently being upgraded with support of the EC QA4ECV and GAIA-CLIM projects in view of upcoming challenges of the Copernicus Sentinel missions.

This work reports on the use of the Multi-TASTE QA/validation system in support to the continuous evolution of operational Envisat data products on ozone, greenhouse gases and temperature, with a focus on the latest upgrades of GOMOS (IPF V5 to IPF V6), MIPAS (IPF V5 to ML2PP V7), and SCIAMACHY (SGP V3 to SGP V6) processors. The studies conclude with altitude and latitude-resolved estimates of bias, spread, and long-term stability of those latest versions.

Formaldehyde (HCHO) is a key component in the chemistry of the troposphere deriving both from anthropogenic and biogenic sources. During the last decades HCHO can be measured from space in the near-UV by solar backscatter instruments. HCHO column measurements offer the potential of identifying photochemical hotspots in the atmosphere and understanding of biogenic emissions, biomass burning and urban pollution. GOME-2/MetOpA mean monthly HCHO observations are compared to simulated HCHO columns from the CAMx chemical transport model. The major features of the retrieved and simulated formaldehyde column distribution are discussed and compared with previous HCHO datasets over the major emission regions. Finally, we exploit satellite HCHO retrievals and the model data to test the ability of observations to reproduce geographical and seasonal variability in HCHO emissions over Europe.

Spatiotemporal distributions of sulfur dioxide (SO₂) and formaldehyde (HCHO), two important short-lived precursors of aerosols and ozone, offer key information on the air quality and interaction between atmospheric chemistry and climate change. Satellite measurements represent the only viable approach for obtaining such information on a global scale. The TROPOspheric Monitoring Instrument (TROPOMI) aboard ESA's Sentinel-5 Precursor (S5P) mission will extend and enhance global trace gas measurements carried out by the Ozone Monitoring Instrument (OMI) flying on NASA EOS Aura spacecraft. TROPOMI boasts ground resolution a factor of ~six greater than OMI, making it an optimal instrument for measuring weak SO₂ and HCHO signals in the boundary layer.  To take full advantage of the capabilities of TROPOMI, retrieval algorithms need to be thoroughly evaluated against independent methods/datasets.

Our team at NASA Goddard Space Flight Center is responsible for the operational SO₂ product from Aura/OMI that has been widely utilized by both science and application communities. We are currently producing the new generation OMI SO₂ product using an innovative principal component analysis (PCA) algorithm. We have also demonstrated HCHO retrievals from both OMI and SNPP/OMPS by applying our PCA approach. The PCA algorithm directly derives spectral features from the measured radiances to account for various interferences in SO₂ and HCHO retrievals such as ozone absorption and rotational Raman scattering (RRS). This data-driven approach is conceptually very different from the official TROPOMI SO₂ and HCHO algorithms based on Differential Optical Absorption Spectroscopy (DOAS). The main goals in our team’s participation in the S5P validation team are to evaluate and improve the quality of the official TROPOMI SO₂ and HCHO products. To this end, we will make thorough intercomparison between the official products and our independent PCA retrievals. For independent S5P SO₂ validation we will conduct balloon SO₂ measurements near Turrialba volcano in Costa Rica. We will use a dual ECC sonde system, where an additional sonde is flown on the same payload using a selective SO₂ filter. The difference of the measurements in the dual sonde is a direct measure of the amount of SO₂ encountered as opposed to the inferred estimate that can be made with a single ozonesonde.

Satellite data can be used to retrieve the spatial and temporal variation of aerosol properties. Passive instruments (radiometers) provide this information integrated over the atmospheric column, such as aerosol optical depth (AOD) at various wavelengths. In the ESA DUE Globemission and the EU FP7 project Marco Polo the spatial information is used together with inverse modelling to provide top-down information on aerosol emissions. In this contribution we focus on China where in addition to passive instruments also CALIOP, an active instrument (lidar), is used to provide information on the vertical variation of the aerosol properties as well as on the occurrence of several different aerosol types. Several passive instruments, in particular MODIS and AATSR, together with a merged AOD product based on MODIS, MISR and SeaWiFS, are used to provide the spatial AOD which is combined with CALIOP data to describe the 4D variation of aerosols over China from 2002. The results are used to identify changes in aerosol properties during the last two decades including spatial and temporal trends. Anomaly plots are used to identify regions where aerosol concentrations are changing and the results can then be used to further study the possible reasons for these changes such as evolving anthropogenic activities or measures to reduce emissions. AOD time series will be compared with those from precursor gases and model estimates.

SO₂ emissions are a good indicator for volcanic activity, since besides weak anthropogenic emissions there are no other known sources for atmospheric SO₂. Furthermore it can be a proxy for the much harder to detect volcanic ash, which can be hazardous not only for the local population but also for aviation.

Under the leadership of IMF, DLR-EOC provides operational trace gas measurements, including total SO2 columns, in near-real-time (i.e., within 2 hours of recording) in the framework of EUMETSAT’s Satellite Application Facility on Ozone and Atmospheric Chemistry Monitoring (O3M-SAF). After the launch of the Sentinel-5 Precursor mission, DLR-EOC will also provide near-real-time total SO₂ columns based on data from the TROPOMI instrument.

We will present here first results of the improved operational GOME-2 SO2 retrieval code GDPv4.8 as well as latest results of recent volcanic eruptions detected by GOME-2 aboard MetOp-A & -B.

The first phase of the ESA Ozone_cci project led to the release of new total ozone data records derived from the GOME/ERS-2, SCIAMACHY/Envisat and GOME-2/Metop-A instruments using the GODFIT v3 level-2 retrieval algorithm. An unprecedented level of consistency and stability was achieved from these sensors through application of an original soft-calibration procedure making use of reference Brewer spectrophometer data.

As part of the second phase of the Ozone_cci project, the GODFIT algorithm has been applied to OMI, and the full level-1 archive for that instrument has been reprocessed using a fast radiance look-up table implementation of the code. Validation results have shown that the GODFIT OMI total ozone data set processed without any soft-calibration is of remarkable stability. This result was used to design a new approach, which uses the combined GOME and OMI data series as a long-term reference, to soft-calibrate the other sensors. This approach allows us to maintain the independence of the satellite data sets against ground-based references. Additional algorithmic developments have also been carried out, including an improved treatment of the instrumental slit functions and their time variation, an optimized correction for the solar I₀-effect, an investigation of alternative a priori ozone profile data bases, and an improved error characterization. We discuss the impact of those developments on the quality of the CCI total ozone data products to be regenerated in 2016.

In the context of the IDEAS+ support contract (ESA/ESRIN SPPA) and in the framework of the PANDSONIA project (ESA), the Physics Department of Sapienza University of Rome and ESA/ESRIN EOP-GMQ section have set-up a joint instrumental suite for validating the atmospheric chemical and optical characteristics retrieved from satellite, and the studies about Planetary Boundary Layer (PBL).
Ground based active and passive remote sensing instruments are operating in synergy, in both a urban context (University of Rome) and in a rural environment (ESA/ESRIN). This instrumental set-up composes a so called “Super Site”, offering quantitative and qualitative information for a wide range of atmospheric parameters in a polluted areas such as the Rome city centre, compared to a rural environment.
The list of the BAQUNIN Super Site instrumentation comprises: Raman and elastic LIDAR systems operating day and night (aerosols, H2O, clouds), SODAR (wind profiles in PBL), MFRSR radiometer (aerosols, O3 ,H2O), POM 01 L Prede sun-sky radiometer (aerosols, precipitable water content), Brewer spectrophotometer (O3, SO2, NO2), Pandora Spectrometers (O3, NO2, H2O, aerosols), CIMEL photometer (aerosols), YES broad-band UV radiometer, and meteorological sensors (for air temperature and relative humidity measurements).
The atmospheric data acquired during BAQUNIN lifetime will be made available to the scientific community, and will contribute to the validation of the aerosol and tropospheric trace gases products produced by the Copernicus Sentinel-5p, Sentinel 4 and Sentinel 5.
In this work, the BAQUNIN Super Site structure and operation strategies will be described in details.

The variations in the spectrometer slit-function of the Sciamachy instrument will give rise  to systematic bias in the retrieved science products. The slit-function characterization of the instrument is investigated over the instrument's lifetime. The retrieval of the slit-function parameters is emphasized towards the UV spectrum since we are interested in retrieving the vertical ozone profile from a spectral range of 265 - 330 nm. This will be extended to the entire wavelength range in future. We run an optimal estimation routine using the initial information on the SCIAMACHY slit function (derived from various studies performed during the on ground calibration of SCIAMACHY) and find that the retrieved slit-function parameters have changed compared to the values available at the instrument's launch.

A novel algorithm for the joint retrieval of surface reflectance and aerosol properties is currently being developed and tested by Rayference and The Inversion Lab. This algorithm, named Package for the joint Inversion of Surface and Aerosol (PISA), includes a fast physically-based Radiative Transfer Model (RTM) accounting for the surface reflectance anisotropy and its coupling with aerosol scattering. This RTM explicitly solves the radiative transfer equation during the inversion process, without relying on pre-calculated integrals stored in LUT, allowing for a continuous variation of the state variables in the solution space. For each processed spectral band, PISA delivers the surface Bidirectional Reflectance Factor (BRF) and aerosol optical thickness, discriminating the effects of small and large particles. It also provides the associated uncertainty covariance matrix for every processed pixels. 

 

The potential of PISA for processing Proba-V data will be analysed in this presentation. PISA requires multi-angular observations for the retrieval of surface BRF which can be obtained from a temporal accumulation of Proba-V 1km observations. The present work specifically focuses on the possibility to derive aerosol properties from Proba-V simulated observations with PISA over different surface types. For these processed cases, the information content of each Proba-V band will be analysed based on the prior and posterior uncertainty covariance matrices. The analysis will demonstrate in particular the capability of PISA to decouple the fraction of the TOA BRF signals coming from the surface reflectance from the one originating from the aerosol scattering.

Satellite observations play an important role in atmospheric science especially in air quality monitoring. The new generation of satellite instruments like the Global Ozone Monitoring Experiment 1 and 2 (GOME-1 and 2), the Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCHIAMACHY), and the Ozone Monitoring Instrument (OMI) allow to retrieve information of tropospheric trace gases like Formaldehyde (HCHO), Nitrogen dioxide (NO₂) etc. on a global scale. But these data products have still quite large uncertainties. Therefore the validation of those satellite products is very important to quantify and improve their quality. Usually ground-based measurements are used for this validation work. These measurements are mostly performed at one fixed location and usually represent a much smaller area than the (typically much larger) satellite footprints. Thus horizontal gradients, which are typical close to strong emission sources, have a strong effect on the validation results. Mobile MAX-DOAS measurements of tropospheric trace gases are one tool to cover the whole extent of a satellite ground pixel in a short period of time. This makes it possible to map existing gradients and thus improve the validation results.

One way to perform mobile ground-based measurements is to perform mobile Multi-AXis-Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements using cars. We mounted two MAX-DOAS instruments on the roof of a car looking in two different directions (one forward and one backward). The measurements are performed in Romania in August/September 2015 and Germany from October 2015 till March 2016. The recorded spectra are analyzed for NO₂ and HCHO using the DOAS technique. First results show that we are able to reveal sharp gradients of NO₂ and also HCHO within the city of Bucharest/Romania. We present a comparison of the obtained tropospheric column densities of NO₂ and Formaldehyde to satellite data from OMI and GOME-2.    

The effects of aerosol on clouds over land is investigated using data from the SAMBBA field experiment and a Large Eddy Model (LEM). The observational data are from instruments flown on the UK’s BAe-146 aircraft over Brazil during the SAMBBA campaign in the autumn of 2012. The variation of east to west and the dry SAMBBA phase to wet SAMBBA phase effects in the west are presented. In addition, we study the variation in the altitude of the aerosol with respect to the cloud and compare the indirect versus the direct plus semi-direct radiative forcing.

TROPOMI will be launched in spring 2016 as part of the ESA Sentinel 5 Precursor (S5P) satellite mission and will provide daily global observations of a number of key atmospheric trace gases with a spatial resolution of 7x7 km². Within the S5P project, BIRA-IASB is in charge of the development of the prototype algorithm for the retrieval of formaldehyde (H₂CO) total tropospheric columns.  H₂CO is a central molecule of tropospheric chemistry. Its observation allows for the quantification of non-methane hydrocarbon emissions, which are, in the presence of NOx, precursors of tropospheric ozone. Simultaneous observations of H₂CO and NO₂ can be used as an indicator of the instantaneous ozone production rate, an important parameter for air quality applications. Owing to its unprecedented spatial resolution and spectral performance, TROPOMI/S5P will significantly improve the monitoring capability of H₂CO and NO₂ from anthropogenic and natural emissions. In this work, we investigate the potential of the S5P prototype algorithm to detect small scale H₂CO source points based on OMI measurements performed in the so-called “spatial zoom mode”. We discuss the impact of spatial resolution effects on H₂CO column retrievals under different conditions of sampling and also address the potential of combining NO₂ and H₂CO  observations to infer information on ozone production in the lower troposphere.  

This paper presents the C3S F4P Benchmark Module Platform.  The C3S Climate Data Records (CDS) include various Essential Climate Variables (ECVs) that are derived from space sensors, including from Copernicus Sentinels sensors. One module of the C3S F4P platform focuses on the benchmarking of data sets towards the understanding why there are discrepancies between CDS records.

We present here various methods for helping at summary the inter-comparisons results, such as the gamma index.  This latter is a quality estimator of the consistency between two distributions (a reference and a test). The method is routinely used in medical physics and earlier used in remote sensing (Voyant, Cyril, et al. 2014). Gamma test was first introduced by Low et al., 1998 as a normalized metric that combined pixel intensity difference (PID) and distance to agreement (DTA, e.g. distance between iso-intensity curves) tolerance terms, while performing robustly in the regions where those are prone to failure. Conceptually, gamma combines PID and DTA into a non-dimensional metric that can be seen as the minimum Euclidean distance in a normalized intensity pixel-distance space. This interpretation is based on the assumption that sometime the discrepancy between measurements may not be due to an error of the map calculation algorithm or delivery hardware, but simply due to experimental error (different offsets or spatial resolutions). In the high gradient region, a small offset could induce a significant error between actual pixels. Each pixel of the distribution to benchmark is associated with a gamma index value. Regions, for which the gamma index is higher than 1, correspond to the locations where the calculation does not meet the acceptance criteria. The normalized number of pixels for which gamma test is passed can provide a valid indicator of the consistency between the two data-sets. Preliminary results using land surface albedo are presented. 

SCIAMACHY is a passive imaging spectrometer mounted on board the ENVISAT satellite to probe a large number of atmospheric trace gas species, such as methane, and their global distribution and evolution. Methane is particularly interesting as it is the most abundant greenhouse gas in the Earth atmosphere after carbon dioxide. To analyze SCIAMACHY methane measurements, we used the DLR BIRRA (Beer InfraRed Retrieval Algorithm) algorithm to retrieve nadir methane concentrations from SCIAMACHY’s channel 6 spectra.  By integrating the DLR BIRRA code into ESA’s operational Level 2 processor, we expanded the processor to include atmospheric CH4 column measurements.

We performed an extensive test and verification operation. Our tests are based on separate comparisons with existing space and ground-based measurements of methane column density. We present here our strategy for quality check of this first version of a CH4 product. We will further discuss specific geographical areas we used to validate the products.

The spatiotemporal variability of surface solar radiation (SSR) is examined over the region of Eastern Mediterranean for the period 1983-2013. It is found that the CM SAF SARAH (Satellite Application Facility on Climate Monitoring Solar surfAce RAdiation Heliosat) satellite-based product is homogeneous compared to ground-based observations, and hence appropriate for climatological studies.  Specifically the CM SAF SARAH data are in good agreement to observations from 5 quality-assured stations in the region. The high spatial resolution of this product allows for studying various local features. Over land, the SSR levels highly depend on the topography, while over the sea, they exhibit a smooth latitudinal variability. The CM SAF SARAH product is also compared against 3 satellite-based products and one reanalysis product. The CERES (Cloud and the Earth’s Radiant Energy System), GEWEX (Global Energy and Water Cycle Experiment) and ISCCP (International Satellite Cloud Climatology Project) satellite-based datasets underestimate SSR while the ERA-Interim reanalysis dataset overestimates SSR compared to CM SAF SARAH. The combined use of a radiative transfer model and a set of ancillary data allows for the attribution of these biases to the atmospheric parameters that drive the transmission of solar radiation in the atmosphere (i.e. clouds, aerosols and water vapor). The CM SAF SARAH SSR trend is positive (brightening) and statistically significant at the 95 % confidence level (0.2 W/m²/year or 0.1 %/year) over the region for the period 1983-2013, being almost the same over land and sea. The CM SAF SARAH SSR trends are closer to the ground-based trends than the trends of CERES, GEWEX, ISCCP and ERA-Interim. This could be possibly due to the high spatial resolution and the better representation of cloud radiative effects in the dataset. We show here that the use of an aerosol climatology for the production of CM SAF SARAH, that neglects the aerosol interannual variability, leads to an underestimation of the SSR trends. We suggest that the inclusion of the interannual variability of aerosol load and composition within CM SAF SARAH would allow for a more accurate reproduction of the SSR trends.

The FAME-C (Freie Universität Berlin AATSR-MERIS Cloud)daytime algorithm is presented, which is developed in the frame of the first phase of the ESA Climate Change Initiative Cloud. During the second phase, several enhancement have been applied, such as a Bayesian cloud mask and the use of a well-established cloud typing algorithm. Cloud products are retrieved using visible, near-infrared and thermal infrared measurements from the Advanced Along-Track Scanning Radiometer (AATSR) and the MEdium Resolution Imaging Spectrometer (MERIS), both mounted on the polar orbiting satellite ENVISAT.                                                                                           

A set of macro-physical, optical and micro-physical cloud properties are retrieved within FAME-C. AATSR measurements in the visible are used to derive daytime micro-physical cloud properties such as cloud optical thickness, effective radius, and liquid water path/ice water path. The cloud top height can be determined from both thermal emission of the cloud using AATSR measurements in the infrared as well as from the average photon path length using MERIS measurements within and near the Oxygen-A absorption band. In cloudy situations this average photon path length is mainly determined by the cloud top pressure.

The FAME-C cloud climatology obtained from the synergistic AATSR-MERIS measurements is evaluated with the help of well-established cloud property data sets obtained from both passive and active measurements from satellite instruments as well as ground-based instruments.

The FAME-C algorithm can be easily adapted and applied to measurements from the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments, both to be mounted on the follow-up mission Sentinel-3 satellite which is planned to be launched in 2016. First efforts towards the adjustments of the algorithm are presented.

Moreover, extensive sensitivity studies have been performed investigating the sensitivity of the two independent cloud height retrievals, using near-infrared and thermal infrared measurements, to cloud vertical distribution. The potential gain in information on cloud vertical distribution due to the three channels in the Oxygen-A absorption band for OLCI is studied using radiative transfer simulations.

Deposition of atmospheric mineral dust is an important factor influencing biogeochemical processes in both oceanic and terrestrial ecosystems. The release of minerals, such as iron and phosphates, stimulates phytoplankton production and plant growth. The Saharan region is responsible for more than half of the global dust emissions and large amounts are transported across the Atlantic year-round along seasonally varying transport routes. In this study, we present a number of observations of dust outflows off the West-African coast with SCIAMACHY. In particular, we retrieve the heights of dust plumes from measurements of the oxygen A band in the near-infrared. Global information on the height distribution of desert dust may help to furter constrain our understanding of these deposition proceses, as well as of many other effects of airborne dust, such as radiative effects, cloud formation processes and impacts on human health and aviation safety.

As preparation for the upcoming Sentinel-5 Precursor mission we are applying the prototype Aerosol Layer Height algorithm to SCIAMACHY. The algorithm makes a spectral fit of the reflectance of the O2 A band from 758 to 770 nm for cloud-free pixels. The shape of the profile assumed in the retrieval is parameterized by a scattering layer with constant aerosol volume extinction and scattering coefficient and with a fixed pressure difference between the top and the bottom of the layer (block profile). The reported height parameter is the mid pressure of the aerosol layer. The main fit parameters are aerosol layer mid pressure and aerosol optical thickness (at wavelengths of the O2 A band).

A set of dust outbreaks from the period 2007-2008 are analysed in detail: retrieved heights are compared with extinction profiles from a recent MACC-II reanalysis run and with lidar measurements made during the SAMUM-2 campaigns at Cape Verde. The SAMUM-2 campaigns took place in January to February and in May to June of 2008 and they thus cover the distinct winter and summer transport modes of African dust across the Atlantic. We also present a number of sensitivity experiments with SCIAMACHY spectra and with simulated data. Here, the focus is on the effect of the assumed profile on the retrieved height parameter in relation to the actual aerosol extinction profile. This is important for understanding the applicability and geophysical sensitivity of the Aerosol Layer Height product. Finally, we will discuss alternative profile parameterizations.

The operational TROPOMI Aerosol Layer Height product will be particularly suited for the retrieval of vertically localized and optically thick aerosol layers in the free troposphere. The same retrieval concept can be applied to the Sentinel-4 and Sentinel-5 missions. In addition to its general scientific value for climate research, Aerosol Layer Height will also help to improve interpretation of the UV Aerosol Index, which is provided by TROPOMI as well.

The OSIRIS instrument has been in operation onboard the Odin spacecraft since the autumn of 2001. During the past 15 years OSIRIS has routinely made vertically resolved measurements of spectrally dispersed limb scattered sunlight. From these measurements very high quality ozone number density profile information has been inferred resulting in the generation of a fifteen year data record that can be used for the study of ozone trends as well as the response of ozone to other forcing terms like the QBO and solar activity. On its own the OSIRIS data record is too short to accurately determine the response to forcing terms with time scales like those found in the solar cycle. Therefore, in order to more accurately infer linear trends and responses in ozone that result from other forcing terms it is beneficial to merge the OSIRIS and SAGE II data records to produce a single time series that covers more than three decades. Results from the SAGE II – OSIRIS data record have been presented in the past and this paper will update these results using the latest OSIRIS ozone data product, Version 5.10. Major changes incorporated into Version 5.10 include an updated instrument model that more accurately represents the time and temperature dependent OSIRIS spectral point spread function and a pointing correction that fixes an error related to the tangent height registration for the OSIRIS line of sight radiance measurements. The impacts on the stratospheric ozone trends of these improvement to the OSIRIS data product will be discussed within this paper.

The Payload Data Ground Segment (PDGS) is the part of the ground segment of the ADM-Aeolus mission that receives the data from the satellite, processes it up to level 1B, 2A and 2B and makes the products available to the user community; it is also in charge of ALADIN instrument planning (including monitoring and calibration).

This poster will present the ADM-Aeolus PDGS from four different perspectives: the functional breakdown, the geographical distribution, the data processing concept and the data access from the users.
The orchestration model, the Near Real Time (NRT) aspects and the cooperation with ECMWF for the generation of level 2B products are also described.

Global Ozone Monitoring by Occultation of Stars (GOMOS) on board Envisat has performed about 440 000 night-time occultations during 2002–2012.  Self-calibrating measurement principle, good vertical resolution and the wide vertical range from the troposphere up to the lower thermosphere make GOMOS profiles interesting for different analyses. Vertical profiles of ozone, NO₂, NO₃ and aerosols are retrieved from night-time UV-VIS spectrometer measurements.

 

The GOMOS ozone data are of high quality in the stratosphere and the mesosphere, but the current operational retrieval algorithm (IPF v.6) is not optimized for retrievals in the upper troposphere–lower stratosphere (UTLS). In particular, validation of GOMOS profiles against ozonesonde data has revealed a substantial positive bias (up to 100%) in the UTLS region. The retrievals in the UTLS are challenging because of low signal-to-noise ratio and the presence of clouds.

 

In this work, we present two advanced GOMOS algorithms, which are optimized for retrievals in the UTLS. One of the algorithms relies on the operational two-step inversion (the spectral inversion followed by the vertical inversion), while another uses one-step approach, i.e., direct fitting the profiles of all constituents. In the new retrievals, a special attention is paid to outlier filtering, the choice of aerosol extinction model, and the vertical resolution.

 

The validation of new retrieved ozone profiles with ozonesondes has shown dramatic reduction of GOMOS ozone biases in the UTLS, for both algorithms. The new GOMOS ozone profiles are also in a very good agreement with measurements by MIPAS, ACE-FTS and OSIRIS satellite instruments in the UTLS. The known geophysical phenomena in the UTLS ozone are well reproduced with the advanced retrievals. In addition, we present GOMOS ozone profiles during the unprecedented stratospheric ozone loss in the Arctic in 2011.

 

These studies have been performed in the framework of ESA-funded ALGOM (GOMOS Level 2 algorithm evolution studies) project. New GOMOS data will be made available via the ESA website.

Flaring is an oxidation process used to dispose off unwanted gases that is extensively used in the upstream oil and gas industry. It is estimated that between 140 and 170 billion cubic meters were flared yearly between 1994 and 2008. The combustion of that associated petroleum gases emits mainly carbon dioxide and methane, but also black carbon, and therefore contributes to global warming. The contribution of flaring to the global methane and carbon dioxide emissions are highly uncertain due to the difficulty of a global monitoring of this source. Currently used mass balances estimate the contribution of gas flaring to 1% and 4% of the global carbon dioxide and methane emissions, respectively. On a local scale, however, it was estimated that gas flaring is the major emitter of black carbon at high latitudes. After emission, black carbon may deposit onto highly reflective snowy/icy surfaces and alter their reflective properties.

The visibility of flares from space at night was first reported in the early 1970s. Such particularity was used in the first decade of the 2000s within a semi-automatic method for the identification of flares and the estimation of their emissions. Being a high-temperature process, flaring emits thermal radiation and some methodologies were developed based on the algorithms used for wildfire detection. Night-time remote-sensed infrared (IR) data have previously helped in identifying gas flares and attempts have been made at quantifying their emissions. However, while wildfires are ephemeral in time but large in size, flares tend to be permanent and small. SLSTR on board Sentinel-3 will have the advantage of a high spatial resolution. Also, gas flares generally burn at higher temperatures than wildfires, the thermal output, which obeys Planck’s law, will have the peak emission at shorter wavelengths. The optimal spectral settings are therefore different.

BIRD is a reduced-size satellite that features a two-channels IR sensor with a very high resolution. Its measurements in the thermal IR band are used to identify wildfires and those detections in specific regions where flaring is likely to occur were visually checked and compared to previous flaring locations databases to determine if they were a false detection where a gas flare was active. The database thus build was then used to extract AATSR radiances at those locations and within a concurrent time window. From there, an algorithm for the identification of gas flares will be developed and verified for AATSR data. That step will produce a new database of flaring locations and a similar approach will be conducted for SLSTR Sentinel-3 data. For some gas flares data on emissions or burned volumes are available, that information will be gathered and, if one or more of those flares were identified from AATSR or SLSTR IR radiances, compared to the intensity of the signal in order to derive an empirical process to compute flaring emissions.

The unique properties of the Australian surface and atmosphere necessitate calibration and validation of forthcoming Sentinel products at Australian sites.  Although heterogeneous, Australia’s landcover is paradoxically both largely evergreen and water-limited, while Australian aerosol is characterised by generally low levels by world standards, but with globally significant episodic outbreaks.   Hence, the validation of Sentinel products over Australia will complement corresponding Northern Hemisphere activities.

This presentation outlines the contribution of several CSIRO teams , including validation of land surface spectral reflectance (LSR) and spectral aerosol optical depth (AOD). The existing commitments for Sentinel-2  focus on LSR from mid-reflectance sites, and AOD from  two of the seven AeroSpan/AERONET sites. Since AERONET data is prominent in the planned validation of a variety of Sentinel products, application of data from additional AeroSpan (Australian AERONET) stations will enable wider validation of aerosol retrievals and atmospheric corrections beyond those currently planned for Sentinel-2.  This will be of increasing importance to Sentinel-4 and -5 where air quality monitoring is paramount.

Beyond the present commitments, CSIRO is well placed to contribute to vicarious radiometric calibration of the Sentinel sensors using high-reflectance targets including terrestrial calibration sites designed to meet CEOS LandNet and RADCALNET requirements, with permanent aerosol infrastructure.  A new site was selected using a systematic temporal, spatial and spectral search employing the Australian Geoscience DataCube based on fully preprocessed Landsat data. This site is currently undergoing further evaluation for permanent installation of surface and aerosol instrumentation.

MULTIPLY is a novel airborne high spectral resolution lidar (HSRL) currently under development by a consortium of European institutions from Romania, Germany, Greece, and Poland. Its aim is to contribute to calibration and validations activities of the upcoming ESA aerosol sensing missions like ADM-Aeolus, EarthCARE and the Sentinel-3/-4/-5/-5p, as well as a number of future Earth Observation (EO) mission concepts such as the Earth Explorer 8 (EE8) mission candidates CarbonSat and Fluorescence Explorer (FLEX) which include products related to atmospheric aerosols. The effectiveness of these missions depends on independent airborne measurements to develop and test the retrieval methods, and validate mission products following launch. The aim of ESA's MULTIPLY project is to design, develop, and test a multi-wavelength depolarization HSRL for airborne applications. With its spectral capabilities and dedicated data processing software, this system will deliver the aerosol extinction and backscatter coefficient profiles at three wavelengths (355nm, 532nm, 1064nm), as well as profiles of aerosol intensive parameters (Ångström exponents, extinction-to-backscatter ratios, and linear particle depolarization ratios).

The high-spectral resolution lidar technique is based on separating the backscattered laser light to the broadened spectrum from molecules and the narrow backscatter peak from aerosols. This allows retrieving aerosol extinction and backscatter coefficients with minimum assumptions during both day and night, making it one of the most advanced techniques for active aerosol remote sensing. HSRL systems have been successfully used from the ground for several years, but only recently it has been attempted to adapt them for airborne operation. At present, airborne HSR lidar instrumentation in Europe includes the 355 nm ALADIN demonstrator and the 532 nm DLR HSRL system. A multi-wavelength HSRL system, with extinction retrieval at 355nm and 532nm, has been developed only by NASA in the USA. MULTIPLY has the ambitious aim to apply the HSR technique at three wavelengths. According to the preliminary design plan, its receiving module will be based on the use of Fabry­­-Pérot interferometers (FPI) for filtering the UV and IR wavelengths, and Iodine filters (IF) to perform this separation for the visible channel. The signals polarized parallel and perpendicular to the plane of laser polarization will also be recorded for all three wavelengths. Specific solutions are considered to ensure thermal stability, vibration filtering, eye safety, low weight and size on board the ATMOSLAB aircraft that is based on a Hawker Beechcraft King Air C90-GTx. The set of products delivered by this lidar will enable to classify the observed aerosols in pure types and mixtures with unprecedented detail and retrieve in the future detailed aerosol microphysical properties. This contribution will present MULTIPLY’s preliminary system design, planned capabilities, and envisaged applications.

We present a detailed description of the TEMPO ground systems post the Critical Design Review (CDR). The grounds systems consist of two components, collocated at the Smithsonian Astrophysical Observatory (SAO), the Instrument Operations Center (IOC) and the Science Data Processing Center (SDPC). The IOC will send commands to the instrument, plan and validate commands, monitor the health and status of the instrument, receive and store telemetry, split raw data into science and health and status, generate monitor and store Level 0 data products (L0) and send all data to the SDPC. The SDPC will receive L0 from the IOC and generate Level 1, Level 2 and Level 3 data products. TEMPO L2 algorithms are based on SAO OMI operational L2 processing codes. Estimates of the data volume, processing requirements and storage capacity are addressed. The schedule and timeline of the IOC and SDPC are presented.

The importance of methane as an anthropogenic Green House Gas (GHG) is well recognized in the scientific community, and is second only to Carbon Dioxide in terms of influence on the Earth’s radiation budget (IPCC, 2014) suggesting that the ability to apportion the source of the methane (whether it is biogenic, abiogenic or thermogenic) has never been more important. It has been proposed (Etiope, 2009) that it may be possible to distinguish between a biogenic methane source (e.g. bacteria fermentation) and an abiogenic source (e.g. gas seepage or fugitive emissions) via the retrieval of the abundances of methane isotopes (12CH4 and 13CH4) and through the ratio of ethane (C2H6) to methane (CH4) concentrations. Using ultra fine spectroscopy (<0.2cm-1 spectral resolution) from Fourier Transform Spectrometers (FTS) based on the SCISAT-1 (ACE-FTS) and GOSAT (TANSO-FTS) we are developing a retrieval scheme to map global emissions of abiogenic and biogenic methane, and providing insight into how these variations in methane might drive atmospheric chemistry, focusing on the lower levels of the atmosphere.

Using HiTran2012 simulations, we show that it is possible to distinguish between methane isotopes using the FTS based instruments on ACE and GOSAT, and retrieve the abundances in the Short Wave Infra-red (SWIR) at 1.65μm, 2.3μm, 3.3μm and Thermal IR, 7.8μm wavebands for methane, and the 3.3μm, 7μm and 12μm wavebands for ethane. Initially we use the spectral line database HITRAN to determine the most appropriate spectral waveband to retrieve methane isotopes (and ethane) with minimal water vapour, CO2 and N2O impact. Following this, we have evaluated the detectability of these trace gases using the more sophisticated Radiative Transfer Models (RTMs) SCIATRAN, the Oxford RFM and MODTRAN 5 in the SWIR, in order to determine the barriers to retrieving methane isotopes in both ACE (limb profile) and GOSAT (nadir measurements) instruments, including a preliminary investigation into the effects of clouds, aerosols, surface reflectance and realistic instrument effects on the retrieval of methane isotopes.

The aim of these RTM simulations is to further narrow down the spectral regions (originally identified in the HITRAN assessment) where methane isotopes can/may be retrieved from orbit. The key outputs from the RTM study are absorption and radiance data, which allow us to identify the cleanest methane regions, and the likely SNR achievable in these regions.

Finally we show some of the results of a study where we compare the output from each of the RTMs used in this study (SCIATRAN, ORFM and MODTRAN), in order to gain some confidence and insight into the strengths and weaknesses of the RTM outputs, using MODTRAN as a benchmark. We show how the output from these models differ when run under different simulation conditions; for example by changing the role of aerosols, atmosphere type and surface albedo assuming when viewed from airborne platforms. Following these variations in scenarios, we discuss the results of an investigation into why differences occur between the models. Finally, conclusions are made on the utility and applicability of the output from each of the models, based on the differences observed in the simulation scenarios.

References:

Etiope, G. (2009). Terrestrial Methane seeps and mud volcanoes: a global perspective of gas origin. Marine and Petroleum Geology 26: 333-344.

IPCC, (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

The Geostationary Emission Explorer for Europe (G3E) is a mission concept for a geostationary satellite sounder that targets at constraining the sources and sinks of the greenhouse gases carbon dioxide (CO₂) and methane (CH₄) for continental-scale regions. G3E aims at imaging the ground scene with spatial and temporal resolution of a few kilometers and a few hours, respectively. Such spatiotemporally dense imaging of the greenhouse gas concentration fields above Europe is expected to boost our ability to disentangle anthropogenic emissions from natural source and sink processes and to impose unprecedented observational constraints on surface flux quantification. In support of the retrieval and interpretation of greenhouse gas concentrations, G3E’s grating spectrometers cover a wide spectral range from the near infrared into the shortwave infrared. This facilitates estimates of column-average CO and aerosol abundances providing extra information on air-quality from a geostationary view. A flexible pointing design further allows for selecting focus regions beyond the European continent in order to address the surface flux budgets of other regions of interest such as tropical Africa. We demonstrate G3E’s capabilities in terms of prospective instrument design, observation concept, and retrieval performance.

The number of satellite-borne atmospheric sounders with a high vertical resolution has dropped significantly with a detrimental impact on the monitoring of long term trends for essential atmospheric species like ozone.

To answer this critical situation, the Belgian Institute for Space Aeronomy is presently developing a small mission based on new concepts and/or technologies.

 

The ALTIUS (Atmospheric Limb Tracker for the Investigation of the Upcoming Stratosphere) mission will be designed from a micro-satellite of the PROBA class orbiting in a 700 km heliosynchronous orbit. The payload will consist of three 2D spectral imagers capable of observing the Earth’s atmospheric bright limb from the ultraviolet to the NIR spectral range. Being a very agile platform, ALTIUS will also promote the concept of multi-mode observations by performing solar and stellar occultation observations, across the terminator and in the dark limb region.

The spectrometric technique will be based on Acousto-Optic Tunable Filters (AOTF) and Fabry-Pérot interferometers. The imaging capacity is an essential method to solve the major difficulty associated with the accurate determination of the tangent altitude of the sensed atmospheric region.

 

ALTIUS is presently at the end of a Phase B1, with a successful intermediate design review for the payload and a preliminary design review for the platform. The mission objectives are both operational (with global stratospheric ozone monitoring as a target) and scientific with expected retrievals of NO₂, H₂O, CH₄ concentration profiles, aerosol extinction profiles and PSC detection.

We use the tool of the Detrended Fluctuation Analysis in order to explore the ozone and temperature dynamics in various height levels in the middle atmosphere. The data employed are the Odin’s satellite ozone and temperature profile observations extended from 2001 to 2015. We focus not on the regular cycles of the temperature field and the ozonesphere but on the non-periodical and noise variability. The results obtained reveal the features of the non-linear nature of the middle stratosphere and the lower mesosphere. In addition a plausible interpretation of these features is given.

In this study an effort to produce tropospheric ozone values using SCIAMACHY ozone observations is presented. Tropospheric trace gases content is of great importance for scientific community but also for public as they affect directly air quality. SCIAMACHY was an instrument on board ENVISAT satellite, which was active for about 10 years (2002-2012), generating long time-series of important atmospheric constituents and parameters. A unique characteristic of this instrument was the ability to make almost instantaneous measurements in nadir and limb mode from the same volume of air. This characteristic makes it possible to get vertical stratospheric concentration profiles just over the area of the nadir measurement. Since now, this limb/nadir matching observations have been used for estimating tropospheric NO₂ column. Herewith, we try to apply the Limb-Nadir Matching (LNM) technique introduced earlier by Sierk et al. (2006), on limb and nadir ozone observations made by SCIAMACHY over the greater area of Athens, Greece. We also make use of the total and tropospheric column of NO₂. The results obtained are also correlated with tropospheric ozone columns estimations over Athens, Greece, presented earlier in the literature (Varotsos et al., 2014).

 

References

Sierk, B., A. Richter, A. Rozanov, C. von Savigny, A.M. Schmoltner, M. Buchwitz, H. Bovensmann, J.P. Burrows: Retrieval and Monitoring of atmospheric trace gas concentrations in nadir and limb geometry using the space-borne SCIAMACHY instrument, Environmental Monitoring and Assessment, 120, 65-77, 2006.

Varotsos, C., J. Christodoulakis, C. Tzanis, A.P. Cracknell: Signature of tropospheric ozone and nitrogen dioxide from space: A case study for Athens, Greece, Atmospheric Environment, 89, 721-730, 2014.

Passive remote sensing of a planetary atmosphere requires an understanding for how incident sunlight interacts with aerosol particles. While polarization of the scattered sunlight contains information about the physical and chemical properties of the particles, interpretation of such polarimetric measurements is an extremely difficult problem due to the strong sensitivity of the polarimetric response on particle shape. The vast majority of atmospheric aerosols are mineral-dust particles, having highly irregular shapes and, therefore, their light-scattering response cannot be adequately modeled with existing fast computational approaches. Here, we propose an alternative approach to characterize light-scattering response from such complex particles. As part of this work, we conduct a comprehensive comparative investigation of the measured polarimetric response for dust particles suspended in air and the same particles deposited on surface. Our study could be useful for the interpretation of Earth-observation data from satellite and spacecraft platforms. In particular, using our experimental approach we aim to quantify the interrelation between responses for single-scattering particles and particles deposited on a substrate. Based on the polarization measured for atmospheric aerosols, this effort may make it possible to predict the polarimetric response for the case when the same particles are deposited. Then, through a study of the polarization observed from a planetary surface, one could localize the source region for the aerosols on the ground. Generally, a reliable localization of such source regions will dramatically simplify retrieval of the particle’s physical and chemical properties. For example, based on a geologic analysis of the source region, one can estimate the predominant chemical composition of the aerosol particles [1].

While the ability to model light scattering by a single irregularly shaped dust particle has had significant progress, the modeling of light scattering from particle-covered surfaces remains primitive. For instance, currently available methods are applicable to very sparsely covered surfaces consisting of spheres particles only. Therefore, laboratory measurements of light scattering by mineral dust particles still plays an important role in interpretation of the light-scattering response from dusty surfaces. Meanwhile, laboratory measurements of aerosol particles provide important supplementary information on the single-particle scattering response. The measurements in our work are conducted at two facilities: gonio-spectro-polarimeter FIGIFIGO located at Finnish Geospatial Research Institute (FGI) [2] and the IAA Cosmic Dust Laboratory (CODULAB), at the Instituto de Astrofísica de Andalucía (IAA) [3]. The FIGIFIGO facility is designed to measure the light scattering response from particulate surfaces, while the CODULAB facility is aimed at measuring the full scattering-matrix of dust particles suspended in air.

We present the results of laboratory optical measurements of various powder samples of natural origin such as clay, volcanic sand, and road dust. These substances can be abundant in the Earth and atmosphere. Using the data obtained with FGI and IAA experimental setups, we conduct a comparative analysis of the intensity and degree of linear polarization of light scattered by aerosol particles and particulate surfaces. Furthermore, from the light-scattering responses measured in the single-scattering regime, we retrieve the refractive index of several samples whose chemical composition is either poorly known or is too complicated to be inferred analytically from their average refractive index.

References:

Chow, J. C., et al. Similarities and differences in PM₁₀ chemical source profiles for geological dust from the San Joaquin Valley, California. Atmospheric Environment 37, 1317-1340, 2003.

Peltoniemi, J., et al. Technical notes: A detailed study for the provision of measurement uncertainty and traceability for goniospectrometers. J. Quant. Spectrosc. Radiat. Transf., 146, 376–390, 2014.

 Muñoz, O., et al. Experimental determination of scattering matrices of dust particles at visible wavelengths: The IAA light scattering apparatus. J. Quant. Spectrosc. Radiat. Transf., 111, 187-196, 2010.

The GOMOS (Global Ozone Monitoring by Occultation of Stars) spectrometer on Envisat measured more than 860 000 ozone profiles during its ten years of life. These measurements cover all latitudes and extent from the troposphere up to the mesosphere. The best data quality is obtained from the nighttime measurements and these measurements have been used to create ozone climatologies and times series. In addition to ozone, also NO₂, NO₃, H₂O, O₂, and aerosol extinction profiles are retrieved from GOMOS spectrometer data. High resolution temperature profiles are determined from the time delay of signals from two fast photometers. GOMOS measurements of NO₂ and NO₃ have provided first night climatologies of these strongly diurnally varying constituents. In the polar areas NO₂ observationshave shown how energetic particle precipitation interacts with the middle atmosphere. Aerosol extinction data and photometer signals have been used to monitor polar stratospheric clouds and mesospheric noctilucent clouds.

 

In the ESA’s GOMOS Level 2 algorithm evolution studies-project we aim at improving the quality of GOMOS products and increase their value in the following way: 1) Improve the GOMOS ozone retrieval in the upper­ troposphere-lower stratosphere region where a large bias between ozone from GOMOS and ground based instruments is evident. The methods tested are a further development of the operational algorithm and a new one-step retrieval algorithm. 2) The retrieval of H₂O and O₂ from IR-bands has turned out to be challenging. With the change of the retrieval algorithm and developing the wavelength assignment we try to improve the quality of H₂O and O₂ results. 3) Light emitted by stars and transmitted through the Earth’s atmosphere to GOMOS UV-visible and IR wavelength detectors include fingerprints from additional minor gases not found in the operational GOMOS products. The poor signal-to-noise ratio does not allow retrieval of these gases from individual occultations, but averaging several occultations these fingerprints become visible. A new GOMOS product of averaged transmissions will be generated. 4) We aim to facilitate the use of GOMOS products by creating user-friendly data sets from original GOMOS Level 2 profile data and by generating new altitude gridded products for ozone, NO₂, NO₃ aerosols, H₂O and O₂.

Atmospheric aerosols have a large effect on the climatic forcing. They generally tend to cool the atmosphere directly by reflecting incoming solar radiation back to space. They also have a number of, mostly cooling, indirect effects via aerosol-cloud interactions.

A viable way to determine the aerosol properties is to use measurement instruments on-board space-borne satellites. Column integrated aerosol properties are retrieved by using the satellite measured radiance or reflectance. The main aerosol property is the aerosol optical depth (AOD) which is connected to aerosol optical properties, size distribution, and loading. Currently satellite retrievals begin to be significant in climate studies as properly validated global data sets are becoming available.

It is possible to to achieve global coverage of the satellite retrieved aerosol distribution in principle. In practice, however, spatial coverage is limited severely by clouds and illumination geometry. Clouds inhibit retrievals completely for passive instruments that measure reflected solar radiation; these instrument cannot see through clouds. The geometry limitation means that it is not possible to have reliable measurements when sun is very low on the horizon (large values of solar zenith angle) which occurs during local winter periods at high latitudes. The temporal coverage of the satellite retrievals is limited by the width of the swaths of the instruments. For example, he ESA's ATSR2/AATSR instruments with their 512 km swath leads to an overpass every 3-5 days.

The main interest in this study is the aerosol data set retrieved using the ATSR2/AATSR reflectance measurements. This set begins from year 1995 when ATSR2 started it's operations to year 2012 when AATSR ceased to provide data. This data set  is currently the longest validated data set of atmospheric aerosol properties. In addition, the data set will be prolonged when the Sentinel-3 is launched in the near future. Sentinel-3 will have on-board the SLSTR instrument which will continue the ATSR2/AATSR aerosol data set.

In this work we are laying the groundwork for the proper sampling of the data for satellite retrieved aerosol properties so that the current and future satellite data can be reliably used in climate studies. The results of the work are:

Describe the sampling procedure for two different satellite aerosol data sets (AATSR/ATSR2, MODIS) to establish the ground rules about how satellite data should be treated in climate studies.

Assess the differences in the sampled data to determine the true difference between the satellite instruments and a combined data set determined using data from both instruments. In addition, the satellite data is compared to ground based data.

Trends and uncertainties are determined and shown for the ATSR2/AATSR data set using statistical analysis.

Producing the basic parameters of the estimation of precipitable water vapor and obtaining precipitable water vapor with the same accuracy as radiosondes are qualifications for creating an infrastructure of GNSS Meteorology. The precipitable water vapor data obtained continuously with the same accuracy as radiosonde for all of Turkey will be used as a basis data or research on the weather and hydrological disaster prediction, long-time hydrological cycle, atmospheric chemistry and global climate. In order to provide for daily GNSS observations a continuous GNSS network of Turkey, TUSAGA-Active (also CORS-TR), has been be utilized for GNSS meteorology study since this network was established for the determination of positions and monitoring plate tectonics.

 In GNSS meteorology, tropospheric zenith delay (ZTD) plays an important role in the estimation of the precipitable water vapor. GNSS derived total tropospheric zenith delay has two parts: the dry zenith delay (ZHD) and the wet zenith delay (ZWD). ZWD is calculated by subtracting ZHD from ZTD. Then it is converted to the precipitable water vapor (PWV) by using the weighted mean temperature or Q models and the surface meteorological data such as temperature, pressure and humidity of a GNSS station.

Study area is chosen in the north west of Turkey encompassing 38.0°–42.0° northern latitudes and 28.0°–34.0° eastern longitudes. Aim of this study is to derive temperature, pressure and humidity observations using spherical harmonics modelling and to interpolate for the derivation of precipitable water vapor (PWV) in the test area. In the computations, the Tikhonov regularization algorithm is used to solve for the ill conditioned problem occurring in small areas. It has been found that the precision of modeling and interpolation depends on the measuring accuracy of meteorological data, density and distribution of meteorological station network. In conclusion, the meteorological parameters computed by using GNSS observations for the study area have been modelled with a precision of ±1.74 K in temperature, ±0.95 hPa in pressure and ±14.88 % in humidity. Considering studies on the interpolation of meteorological parameters, the precision of temperature and pressure models provide adequate solutions. On the other hand; the precision of the humidity model does not provide adequate solutions. Hence, it has been found that the precision of GNSS derived PWV is ±0.50-1.32mm. It shows that it yields PWV with precision close to the precision of radiosonde measurements which is about 1–2 mm.

This study funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (The Estimation of Atmospheric Water Vapour with GPS Project, Project No: 112Y350).

The Meteosat satellites play an important role for the generation of consistent long time series of aerosol properties. This importance relies on (i) the long duration of past (Meteosat First Generation, MFG), present (Meteosat Second Generation, MSG) and future (Meteosat Third Generation, MTG) missions and (ii) their frequent cycle of acquisition that can be used to document the anisotropy of the surface and therefore the lower boundary condition for aerosol retrieval over land surfaces.

 

The novel algorithm for the joint retrieval of surface reflectance and aerosol properties developed by Rayference and The Inversion Lab, named Package for the joint Inversion of Surface and Aerosol (PISA), is applied on TOA BRF acquired by SEVIRI onboard Meteosat Second Generation (MSG) in the VIS0.6, VIS0.8 and NIR1.6 spectral bands. PISA relies on the inversion of a physically-based radiative transfer model accounting for the surface reflectance anisotropy and its coupling with aerosol scattering. SEVIRI observations are accumulated during several days to document the surface anisotropy and minimize the impact of clouds. While surface radiative properties are supposed constant during this accumulation period, aerosol properties are derived on an hourly basis.

 

The information content of each MSG/SEVIRI band will be provided based on the analysis of the posterior uncertainty covariance matrix. The analysis will demonstrate in particular the capability of PISA to decouple the fraction of TOA BRF signal coming from the surface from the one originating from the aerosols. This analysis will provide valuable learning that will help designing the processing chain required to perform a multi-year retrieval over the whole SEVIRI disk. This will thus constitute a first achievement in the development of the option “SEVIRI hourly aerosol product” of the aerosol_cci2 project.

Atmospheric dynamics is a complex topic that involves observational and theoretical analysis of all motion systems of meteorological significance, including several different phenomena such as thunderstorms, tornadoes, lee waves, tropical/extratropical hurricanes, etc. In the frame of the ESA Earth Observation Sensor Performance, Products and Algorithm (https://earth.esa.int/web/sppa/) activities, the detection of one of these atmospheric phenomena, in particular the lee waves, has been studied and is presented in this paper.

The lee waves, commonly known as mountain waves, are generated during wind flow over obstacles like mountains under certain atmospheric conditions such as wind flow perpendicular to the mountain, increasing wind velocity with altitude (and wind velocity of at least 20 knots at mountain top) and either a stable air mass layer aloft or an inversion below roughly 4,600 metres.

This phenomenon can be observed by satellite instruments as well as being reproduced by models. The lee waves phenomenon occurred over the Tyrrhenian sea on 14 April 2008 has been used as case study to show how satellite instruments based on different retrieval techniques can be exploited for the detection of such events. This can be achieved by observing the impact that these waves have on cloud formation (lenticular or rotor clouds if the air contains sufficient moisture) and on certain atmospheric parameters like water vapour, temperature and pressure. In addition, the Weather Research and Forecasting (WRF-ARW) model has been used to assess how numeric simulations initialized with real data and/or analysis data are also able to predict the formation of these waves.

A new version of the NO2 retrieval for OMI - the KNMI DOMINO retrieval code, version 3 - has been recently developed. The modifications in this algorithm compared to the current DOMINO version 2 product will be discussed in our contribution. The spectral fitting to derive the DOAS slant column for NO2 has been revised as explained in the paper by van Geffen et al., AMTD 2014, and the main modifications will be reviewed. The TM4 chemistry-transport model used for DOMINO-2 is upgraded to the latest TM5 release, and the resolution is increased from 3x2 degree to 1x1 degree. Further aspects which were updated include the temperature dependence of the O2-O2 cloud retrieval, the error estimation and terrain height treatment, and newly developed air-mass factor lookup tables. The estimate of the stratospheric NO2 column by means of data assimilation in TM5 is improved. Results with the updated retrieval will be shown. Various aspects of these developments have been verified through intercomparisons with other retrieval approaches within the TROPOMI verification team and the European QA4ECV project. Finally we will discuss the future application of DOMINO-3 for TROPOMI.

The present study aims to validate different approaches for the estimation of three photobiological effective doses: the erythemal UV, the Vitamin-D and that for the DNA damage, through surface-based measurements of solar UV and total radiation since 2005. Data from a UV spectroradiometer, a multifilter radiometer, UV and total radiation pyranometers that are located in Thessaloniki, Greece (40.63^o E, 22.96^o N), are used together with appropriate algorithms and models in order to calculate the desired quantities. In addition to the surface-based doses retrievals, satellite retrievals are also used in order to assess the accuracy of each of the presented methods. The main aim of the study is the investigation of the accuracy of each of the proposed systems/methods on the calculation of the above mentioned photobiological doses.

The calculation of the three photobiological doses are based on measurement of different instrumentation: The Brewer #086 is a double monochromator spectroradiometer that measures the UV solar spectrum (286.5 - 363 nm) with a wavelength step of 0.5 nm, within an interval of 7 minutes. The NILU-UV multi-filter radiometer provides one-minute measurements in 5 UV channels with nominal central wavelength at 302, 312, 320, 340 and 380 nm. Additionally, the YES UVB-1 pyranometer provides one minute measurements of the erythemal dose and the CM21 one minute global (total) horizontal radiation measurements. Appropriate algorithms have been used (direct weighting of individual action spectra for spectral measurements, use of radiative transfer models and use of neural networks for other instrumentation) for each dataset so that the surface-based photobiological effective doses can be derived.

Satellite estimates of erythemal UV, Vitamin-D and DNA damage effective dose time series from the OMI/Aura, SCIAMACHY/Envisat and GOME2/MetopA instruments are also used to provide a comparison for the surface-based estimates. The OMI/Aura surface UV irradiance data from 2004 to 2014 include the erythemally-weighted daily dose and erythemal dose rate both at the overpass time and also at local solar noon. The SCIAMACHY/Envisat and GOME2/MetopA joint UV product, that includes the same sub-products as the OMI/Aura dataset, were also used. Time series analysis and correlation statistics identify the agreement limitations as well as atmospheric, algorithm related and technical factors responsible for sources of discrepancy among the retrievals.

The Meteosat First Generation (MFG) satellites have been acquiring a continuous record of Earth observations for more than 30 years. The Meteosat Visible and Infrared Imager (MVIRI) on-board the MFG geostationary satellites acquires radiance twice per hour in a broad reflectance channel referred to as the visible (VIS) band.

The temporal frequency of observations facilitates to disentangle surface from atmospheric effects and makes the MVIRI data pre-eminently suited for producing long-term data records of surface albedo and aerosol optical depth. The surface albedo data records that have been generated hitherto from MVIRI observations exhibit temporal inconsistencies due to an inaccurate pre-launch spectral response characterisation. This inaccurate characterisation restricts the use of calibrated MVIRI data records for climate applications in general, and prevents a retrieval of aerosol optical depth in particular.

The FIDelity and Uncertainty in Climate data records from Earth Observations (FIDUCEO) Horizon 2020 project aims to reduce and quantify the uncertainty on the instrument spectral response characterisation to facilitate the creation of long-term consistent and-high-quality data records of surface albedo and aerosol optical depth for climate applications. Within FIDUCEO, a novel method for recovering the VIS spectral response and its uncertainty is being developed. In addition, the method is aiming to recover and monitor the spectral degradation of the MVIRI VIS response with time. The accuracy of the method is being assessed by test-wise recovering the spectral response of the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) high-resolution visible (HRV) band, which is essentially similar to the spectral response of the MVIRI VIS band, but has been accurately measured pre-launch. 

The recovery of the VIS spectral response is based on top-of-atmosphere simulated spectral radiance over pseudo-invariant calibration sites like deep convective clouds, bright deserts, and ocean targets viewed under different illuminating and atmospheric conditions. Selected scenarios combine these simulated spectral radiance data with simulated and actual sensor recordings. Inverse modelling methods are applied to recover the sensor spectral response and ageing characteristics, including uncertainties and covariance. Preliminary results are presented.

MVIRI VIS observations have been calibrated vicariously at EUMETSAT using a method that is based on simulated radiance over bright deserts and ocean surface targets, but does not take into account sensor ageing. EUMETSAT will apply the spectral response and ageing characteristics and uncertainties recovered by FIDUCEO, if established, for recalibrating the MVIRI VIS observations and enabling FIDUCEO to generate new fundamental (radiance) and thematic (albedo and aerosol) climate data records from MFG VIS observations.

The detection of climate change and analysis of climate variability from diurnal to inter-annual time scales require long-term, well calibrated observations that are homogenised in time and space. Observations from EUMETSAT's series of Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) geostationary satellites span a period from 1982 to today. Although these satellites provide data for climate analysis at multi-decadal scales, their applicability for such analysis is hampered by heterogeneities in the time series due to successive radiometers having different filter functions and changes in the calibration methodology. EUMETSAT initiated the activity to improve the quality of these data, and generates a Fundamental Climate Data Record (FCDR) of Water Vapour (WV) and Infrared (IR) channel radiances, i.e., a long-term data record of calibrated and quality-controlled sensor data designed to allow the generation of homogeneous products that are accurate and stable enough for climate monitoring. The generation of this FCDR is part of EUMETSATs activities in the European Re-Analysis of global CLIMate observations 2 (ERA-CLIM2) project.

 

We present a method to inter-calibrate the complete time series of WV (6.3 µm) and IR (11.8 µm) channel radiances from MFG-MVIRI and MSG-SEVIRI observations. Our method is based on the principles of the Global Space-based Inter-Calibration System (GSICS). A systematic review of spectral conversion functions, which often dominate the errors, indicates that spectral changes of the WV channel from HIRS/2 to HIRS/3 triples the uncertainty of inter-calibrated METEOSAT WV radiances. We will show that these issues can be circumvented by using HIRS/2, AIRS, and IASI as reference instruments, and thus keeping the uncertainties due to spectral conversion similar throughout the time series. Finally we will present an evaluation of 30 years of recalibrated HIRS, MVIRI and /SEVIRI radiances from the IR and WV channels, and demonstrate their improved suitability for climate applications.

We retrieve atmospheric methane CH4 profiles using the dimension reduction inversion method for the measurements of the ground-based Fourier Transform Spectrometer (FTS). The FTS instrument in Sodankylä, Northern Finland, is part of the Total Carbon Column Observing Network (TCCON), a global network that observes solar spectra in near-infrared wavelengths. The high spectral resolution of the TCCON FTS data provides approximately three degrees of freedom about the vertical structure of CH4 between around 0 and 40 km. The dimension of the inversion problem is reduced by truncating the prior covariance so that smooth and realistic deviations from the prior profile can be sought by estimating three parameters for the profile shape.  We use Bayesian framework and adaptive Markov chain Monte Carlo (MCMC) statistical estimation method to better characterize the full posterior of the solution and uncertainties related to the retrieval. The retrieved profiles are validated against in-situ AirCore soundings which provide very accurate reference up to 20-30 km.  Compared to the retrievals using fixed prior profiles, our method produces better residuals, smaller air mass dependent artifacts, and more accurate total column. The method is presented in a general form, so that it can easily be adapted for other applications such as different trace gases or satellite-borne measurements.

ADM-Aeolus is an upcoming Earth Explorer mission of ESA’s Living Planet Programme, carrying the Doppler wind lidar ALADIN dedicated to global wind profile measurements. The Aeolus Calibration and Monitoring Facility is part of the Aeolus Payload Data Ground Segment (PDGS) located in ESA Esrin. It will be in charge of the generation and archiving of calibration and auxiliary Aeolus mission products needed for the L1 and L2 wind data generation. Moreover, the facility includes the in-orbit maintenance algorithms for on board instrument parameter settings. The facility is involved in both Near Real Time (NRT) and subsequent reprocessing scenarios, providing support for the long-term and interactive data quality monitoring.  The poster describes the main features of the software and the Aeolus operational scenarios.

Observations from microwave sounding instruments have played a key role in operational numerical weather prediction (NWP) and climate research for the last 35 years and will continue to do so for the foreseeable future.  Building on a heritage of cross-track sounding radiometers including MSU,  AMSU-A/-B, MHS and ATMS  the MetOp-SG Microwave Sounder  (MWS) will serve weather and climate applications from launch in 2021,  until at least 2042. 

The primary purpose of the MWS, in the context of operational NWP, is to provide observational constraints on the initial conditions for NWP forecast models through the process of data assimilation. The MWS observations, spanning the spectral range from 23.8 - 229 GHz, provide information on temperature, humidity and cloud.  Data from current microwave radiometers provide a large fraction of the total benefit realised from all satellite observations for weather forecasting. Within NWP systems the MWS measurements will be assimilated directly as brightness temperatures.  The high, and improving, accuracy of NWP systems in representing global temperature and humidity fields drives stringent performance specifications for MWS – in terms of stability, bias characteristics and noise performance of Level 1 products.  For example errors in short range (T+6 hour) forecast fields for tropospheric temperature, expressed as top-of-atmosphere (TOA) brightness temperatures for the relevant temperature sounding channels (MWS-5 to -10), are currently in the range 50-100 mK.  For humidity the model errors are larger, at 1-2 K (as TOA brightness temperatures), and the performance specifications for the relevant humidity sounding channels (MWS-19 to -23) are correspondingly less stringent. Within the Met Office assimilation system, data from six microwave sounders (4 NOAA, 2 EUMETSAT) are assimilated operationally.  Recent tests adding sounding data from a further three satellites (F-17, -18 and -19 SSMIS) demonstrate further benefit and highlight the value of microwave sounding observations.

In the context of climate science, data from satellites launched during the period 1978 to the present time have been widely used to determine trends in atmospheric temperatures and humidities.   Increasingly, reanalysis studies using microwave data are being used to determine trends in atmospheric state over decadal timescales.

All of these applications (NWP, climate, and reanalysis) have shown the value of, and need for, careful pre-launch characterisation of sounding radiometers and calibration of the resulting Level 1 products to provide accurate brightness temperature data. For MWS, calibration is based on the two-point method, utilising a cold space view and a warm target view, provided by an on-board black body target. However the basic method is supplemented by corrections applied in the ground processing for receiver non-linearity, scan-bias, and radiometric contamination, in particular from the antenna sidelobes in both the earth and cold space views and from the spacecraft. The calibration relies on the instrument characterisation data base, which is constructed using on-ground instrument measurements from radiometric calibration and antenna pattern characterisation campaigns.

The MWS instrument concept comprises a single scanning antenna, covering all the frequency channels, providing a single polarisation for each and with full redundancy for the temperature-sounding channels. This requires a complex quasi-optic network (QON) to split the overall frequency range. A non-uniform scan profile is used to maximise the scene viewing time. A complete QON bread-board pre-development has been constructed and tested. With respect to receiver architecture, direct detection is employed for the 23.8 GHz, 31.4 GHz and 89 GHz channels and super-heterodyne for the 50-58 GHz, 166 GHz, 183 GHz and 229 GHz channels. For the latter, separate receiver back-ends, based on traditional filter bank solutions, are employed, to downconvert into particular channels. Receiver front end pre-developments for Schottky diode detectors and multipliers and GaAs and InP low noise amplifiers have been undertaken.  The Instrument has a high level of autonomy, with sophisticated FDIR, in-order to maintain system availability.

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) is a scanning nadir and limb spectrometer covering the wavelength range from 212 nm to 2386 nm in 8 channels. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002 on the ENVISAT platform. After the platform failure in April 2012, SCIAMACHY is now in the postprocessing phase F. SCIAMACHYs originally specified in-orbit lifetime was double the planned lifetime. SCIAMACHY was designed to measure column densities and vertical profiles of trace gas species in the mesosphere, in the stratosphere and in the troposphere (Bovensmann et al., 1999). It can detect a large amount of atmospheric gases (e.g. O3 , H2CO, CHOCHO, SO2 , BrO, OClO, NO2 , H2O, CO, CH4 , among others ) and can provide information about aerosols and clouds.

The operational processing of SCIAMACHY is split into Level 0-1 processing (essentially providing calibrated radiances) and Level 1-2 processing providing geophysical products. The operational Level 0-1 processor has been completely re-coded and embedded in a newly developed framework that speeds up processing considerably. In the frame of the SCIAMACHY Quality Working Group activities, ESA is continuing the improvement of the archived data sets. Currently Version 9 of the Level 0-1 processor is being implemented. It will include

An updated degradation correction

Several improvements in the SWIR spectral range like a better dark correction, an improved dead & bad pixel characterisation and an improved spectral calibration

Improvements to the polarisation correction algorithm

Improvements to the geolocation by a better pointing characterisation

Additionally a new format for the Level 1b and Level 1c will be implemented. The version 9 products will be available in netCDF version 4 that is aligned with the formats of the GOME-1 and Sentinel missions. We will present the first results of the new Level 0-1 processing in this paper.

The IASI nadir looking thermal infrared (TIR) sounder onboard MetOp-A enables the monitoring of atmospheric trace gases on a global scale. The IR team of BIRA-IASB retrieves profiles of atmospheric CH₄ from IASI spectra with the ASIMUT radiative transfer and retrieval software.

For this symposium we will present a quality assessment of the BIRA-IASB IASI CH₄ product and results of its validation with NDACC ground-based observations. We will show that 1 independent piece of information is retrieved in the altitude range 4-17 km for daytime and nighttime observations.

In addition, as part of the ESA GHG-CCI TIRS optional workpackage, we will present first results of a comparison between the LMD IASI upper tropospheric CH₄ product and the BIRA-IASB IASI CH₄ product on a global scale.

In satellite remote sensing of atmospheric aerosols the Sun-Earth-satellite viewing geometry plays an important role. Although most retrieval algorithms are capable of handling any reasonable viewing geometry in theory, in practice the retrieval performance may depend on the geometry. We have studied the effect of changing viewing geometry to a dual-view aerosol retrieval algorithm.

The AATSR Dual View (ADV) aerosol retrieval algorithm is based on the stereo-viewing capability of AATSR (Advanced Along Track Scanning Radiometer) aboard ENVISAT. In the k-ratio approach the radiances measured in nadir and55º forward views are used to eliminate the surface reflectance contribution from the top of atmosphere (TOA) reflectance, allowing us to retrieve the aerosol contribution without any prior knowledge of the surface reflectance properties. The algorithm is being adjusted for use with the Sea and Land Surface Temperature Radiometer (SLSTR), scheduled for launch in December 2015 aboard Sentinel-3. The two instruments have similar characteristics, but the oblique view viewing direction changes from the forward view of AATSR to a backward view in SLSTR (with respect to the direction of motion of the instrument). In this paper we consider the effects the changing viewing geometry may have on the aerosol retrieval.

The changing viewing angle affects the TOA reflectance in two ways: (i) the aerosol signal changes due to different scattering angles; (ii) the surface reflectance (and thus the k-ratio) will change. There is some evidence of anomalous latitude dependence in the existing ADV aerosol retrievals. We suspect that this anomaly is related to the k-ratio and scattering angle, which are latitude dependent. This anomaly may have unexpected effects on the retrievals with SLSTR, which will have different latitude dependence for the k-ratio and scattering angles.

We have studied the effects of viewing geometry using the Multi-angle Imaging SpectroRadiometer (MISR) data. MISR has nine viewing angles, one in nadir, and four facing forward and backward directions. Although MISR lacks the 1.6 µm channel crucial for our k-ratio approach, two of its wavebands (558 nm and 672 nm) are close to the AATSR channels used in ADV, and can be used to study the effect of the viewing direction.

A customized two-wavelength version of ADV was run with near-simultaneous data from AATSR and MISR. Retrievals with MISR 60.0º forward view and 60.0º backward view were made and compared with AATSR 55.0º forward view retrievals. The results show that the 1.6 µm channel is essential for the ADV algorithm, and the two-wavelength version cannot be used for reliable retrieval of aerosol properties. However, the preliminary studies also show that there are significant differences in performance of ADV between different MISR viewing directions. Further work on the subject is being conducted.

Atmospheric temperature is a key geophysical parameter when dealing with the atmosphere in areas such as climatology and meteorology. In general, thermal emissions of molecular lines (e.g. oxygen, carbon dioxide) can be used for the determination of the temperature profile. The superheterodyne radiometer MTP (Microwave Temperature Profiler) passively detects thermal emission from oxygen lines at a selection of frequencies between 55--60 GHz by scanning the atmosphere from near zenith to near nadir in the flight direction. The MTP instrument was designed to observe the vertical temperature distribution over the upper troposphere and lower stratosphere (UTLS) with a good temporal and spatial resolution. The instrument was originally developed at NASA's JPL and has been recently flown on DLR's Falcon and HALO research aircrafts.

To estimate the temperature profile from microwave measurements (e.g. provided by MTP), the retrieval algorithm TIRAMISU (Temperature Inversion Algorithm for Microwave Sounding) has been developed at DLR and is currently used to conduct the data processing of the MTP measurements. This study performs the retrievals from the MTP data with a focus on the ML-CIRRUS mission. The corresponding retrieval performance is investigated by associated error characterization and external comparisons with other ground-based and satellite observations. These observations are important to resolve a variety of phenomena in the UTLS region and to potentially improve the temperature spaceborne soundings.

We present a comprehensive recalibration of SCIAMACHY (onboard ENVISAT) by (re)modelling the on-ground calibration measurements with the contamination mirror model (Krijger et al 2014). This provides complete information on the polarisation (including circular) sensitivity of the main optical components of the SCIAMACHY instrument: the scan mirrors and the optical bench module.
This complete instrument polarisation sensitivity has been used to derive a scan-angle dependent degradation correction by combining it with an in-flight contamination model (see also: Mirror Contamination in Space: In-flight Contaminant modelling).

 

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric ChartographY) aboard ESA's environmental satellite ENVISAT observed the Earth's atmosphere in limb, nadir, and solar/lunar occultation geometries covering the UV-Visible to NIR spectral range. It is a joint project of Germany, the Netherlands and Belgium and was launched in February 2002. SCIAMACHY doubled its originally planned in-orbit lifetime of five years before the communication to ENVISAT was severed in April 2012, and the mission entered its post-operational phase.
In order to preserve the best quality of the outstanding data recorded obtained by SCIAMACHY, data processors are still being updated. This presentation will highlight three new developments that are currently being incorporated into the forthcoming Version 7 of ESA's operational Level 2 processor:

1. Tropospheric BrO, a new retrieval based on the scientific algorithm of (Theys et al., 2011). This algorithm had been originally developed for the GOME-2 sensor and later adapted for SCIAMACHY. The main principle of the new algorithm is to utilize BrO total columns (already an operational product) and split them into stratospheric VCD_STRAT and tropospheric VCD_TROP fractions. BrO VCD_STRAT is determined from a climatological approach driven by SCIAMACHY O₃ and NO₂ observations. VCD_TROP is then determined simply as a difference: VCD_TROP = VCD_TOTAL – VCD_STRAT.

2. Improved cloud flagging using limb measurements (Liebing, 2015). Limb cloud flags are already part of the SCIAMACHY L2 product. They are currently calculated employing the scientific algorithm developed by (Eichmann et al., 2015). Clouds are categorized into four types: water, ice, polar stratospheric and noctilucent clouds. High atmospheric aerosol loadings, however, often lead to spurious cloud flags, when aerosols had been misidentified as clouds. The new algorithm will better discriminate between aerosol and clouds. It will also have a higher sensitivity w.r.t. thin clouds.

3. A new, future-proof file format for the level 2 product based on NetCDF. Although the final concept for the new format is still under discussion within the SCIAMACHY Quality Working Group, main features of the new format have already been clarified. The data format should be aligned and harmonized with other missions (esp. Sentinels and GOME-1). Splitting of the L2 products into profile and column products is also considered. Additionally, reading routines for the new formats will be developed and provided.

References:
K.-U. Eichmann et al., Global cloud top height retrieval using SCIAMACHY limb spectra: model studies and first results, Atmos. Meas. Tech. Discuss., 8, 8295-8352, 2015.
P. Liebing, New Limb Cloud Detection Algorithm Theoretical Basis Document, 2015.
N. Theys et al., Global observations of tropospheric BrO columns using GOME-2 satellite data, Atmos. Chem. Phys., 11, 1791–1811, 2011. 

SCIAMACHY (Bovensmann et al., 1999) measurements have been used to produce a unique database of different atmospheric parameters during 2002-2012. In this presentation, the latest SCIAMACHY limb ozone scientific vertical profiles, namely the current V2.9 and the new version, are extensively compared with ozone sonde data from the WOUDC database.

The comparisons are discussed in terms of vertical profiles and stratospheric partial columns. Our results indicate that the V2.9 ozone profile data between 20 km - 30 km is in good agreement with ground based measurements with less than 5% relative differences in the latitude range of 90°S-40°N, which corresponds to less than 5 DU partial column differences. In the tropics the differences are within 3% with exception of the tropical Pacific region where an overestimation of more than 10% is observed. However, this data set shows a significant underestimation northwards of 40°N (up to 15%). The newly developed version reduces this bias to below 10% while maintaining a good agreement southwards of 40°N with slightly increased relative differences of up to 5% in the tropics. The results demonstrate an improvement of 2-20 DU in the stratospheric ozone amounts.

[INTRO]. Mount Etna, in Italy, is one of the most active volcanoes in the world and an ideal laboratory to improve volcano ash monitoring and forecasting (Scollo et al., 2012). In the last decade, the Etna activity was characterized by short and repeated eruptive events generating ash plumes, which dispersed in the atmosphere. Different sensors observations can help the scientists in the monitoring and tracing the ash plume. Satellite sensors cannot easily detect fast-dispersed ash plumes usually of few km above the volcano vent, because of the satellite flying altitude, the overpass time and the coverage by clouds weather. In this case the sensitivity of ground-based sensors, a few km far from the eruptive site, such as lidar, can be valid supports in monitoring finest airborne ash particles rapidly dispersed by wind. The value of dispersed volcanic ash concentrations in the atmosphere is crucial to improve volcanic ash forecasting during volcanic crises, taking into account the time-varying discharge rate of different explosive eruptions.

[GOAL]. In this paper we analyse the lidar measurements during two ash emission events of Mt. Etna. We apply the VALR (Volcanic Ash Lidar Retrieval) in order to classify the ash signature in dimensional categories and then to estimate some ash particle features, such as the mass concentration and mean equivalent-spherical diameter. The detection steps is carried out by using a Bayesian approach, trained by the Monte Carlo model-based dataset (Marzano et al., 2014) and the estimation step using specific regressive laws, knowing the dimensional ash classes. Retrieval results are compared with the ash products evaluated by means of volcanic ash dispersal model (Scollo et al., 2012).

[OBSERVATIONS AND CASE STUDY]. In this preliminary work we analyse a first ash emission event of Etna volcano on November 15^th, 2010, characterized by a weak ash plume observed using a scanning lidar operating at 532 nm. The lidar was located 7 km far from the summit craters and moved in azimuth and elevation to analyse different volcanic plume sections. The second eruptive case studied happened on August 12^th, 2011. The ash plume generated reached 10 km (asl) and was observed using the same lidar system and scanning vertically the ash plume. Only the backscatter coefficients in these cases are used as input to VALR algorithm.

[BEYOND THE STATE OF THE ART]. In particular, scanning lidar systems can be complementary to radar systems; in fact radar systems are more sensible to larger particles at lower frequencies and useful to integrate the micron-sized particle measurement. Lidar techniques developed for aerosol particle detection and estimation can be used also, properly adapted, for the retrieval of dispersed ash distributions (Madonna et al., 2011; Scollo et al., 2012; Martucci et al., 2012, Marano et al., 2015).

 

[EXPECTED RESULTS AND NEW ASPECTS]. The lidar observations of dispersed ash particle in the atmosphere can help finding the main microphysical ash features and the areas characterized by a specific mass concentration of smallest ash particles. This information may help quantifying the impact that ash emissions have on aviation safety to prevent the flights in the areas of high ash contamination (2x10^-4 g/m³) in compliance with the International Civil Aviation Organization directives [International Civil Aviation Organization (ICAO), 2010]. This work, starting from the inter-comparison between the VALR algorithm and ash dispersal model, highlights an ash contaminated area less 20% larger considering different dimensional ash particles; this allows us to define a 20% security margin in the interdicted area in comparison with the area obtained applying the ash dispersal model.

Ozone and cloud parameters (cloud fraction and cloud top height) were used to calculate ozone volume mixing ratios in the tropical upper troposphere with the cloud slicing method [Ziemke, 2001]. The retrieval algorithm S5P_TROPOZ_CSA will be used in the operational processing of the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 precursor (S5-P), which is expected to be launched in the middle of 2016.

The method will be presented and results from SCIAMACHY/GOME-2 shown. Two retrieval schemes for ozone are available. Total ozone from WFDOAS [Coldewey-Egbers et al., 2005, Weber et al., 2005] of IUP Bremen and the operational ESA products for SCIAMACHY/ Envisat (2002-2012) and GOME-2/ MetOpA (2007-2015) measurements are used as input for the tropospheric ozone calculations and the results will be compared.

Furthermore the tropospheric ozone dataset is verified using ozone sonde data from tropical stations. The scientific prototype retrieval code is currently being implemented in the operational processor at DLR. Both algorithms agree quite well as expected. Detailed comparisons are presented here.

Natural and anthropogenic emissions of CO₂ and CH₄ often occur on small scales (several km down to a few meters). Examples are the CO₂ release by volcanoes and power plants, as well as CH₄ emissions from large seeps, landfills, coal mine venting and oil/gas production. Quantifying and verifying these emissions by independent, non-intrusive (remote sensing) techniques is required in the context of a better understanding and management of these sources. Recent achievements using the Methane Airborne MAPper (MAMAP) sensor show that CO₂ as well as CH₄ emissions from localized areal and point sources can be derived from column-averaged dry air mole fractions of CO₂ and CH₄ retrieved from airborne passive nadir remote sensing measurements. This new method allows the independent verification of strong total source emissions from localized source areas or and discrete sources. In 2014 the COMEX campaign was executed in California in support of the CarbonSat Earth Explorer mission definition activities, to validate the new approach by combined remote sensing and in-situ data collection over and around landfills, seeps as well as oil/gas fields. The paper will summarise results of the COMEX campaign and will give an outlook on the future development of this unique remote sensing technique, which is also highly relevant in the context of the validation and data interpretation of current and future CO₂ and CH₄ satellite missions like OCO-2, Sentine-5P, MERLIN and CarbonSat.

Water vapour plays an important role in the atmosphere, since its radiative forcing due to its infrared absorptions is three times as large the radiative forcing of CO₂ in the Earth’s atmosphere. Its relation to air temperature makes it thus an important feedback mechanism for the Earth’s climate and is therefore fundamental for climate analysis as well as for weather monitoring.

The observations of the spectrometers GOME, SCIAMACHY and GOME-2 allow retrieving total column water vapour from 1996 until today. Overlapping periods allow to validate the consistency of the time series and to evaluate instrumental and operational differences between the instruments. Changing instrumental properties such as slight changes in the spectral resolution and their effect on the resulting water column densities are evaluated in order to obtain trend information from this data-set. It allows for example observing deviations from the typical seasonal cycles, such as El Niño events.

The operational DLR/MPIC water vapour retrieval uses the absorptions of water vapour and of molecular oxygen in the spectral range from 612-676 nm in order to obtain vertical column densities (VCDs) of water vapour. From the O₂ slant column density (SCD) the air mass factor of the respective observation can be calculated, which is needed for the calculation of the vertical column density. This procedure is robust and easy to implement and is independent of cloud cover and other external data products. It is however subject to large errors for individual observations (especially if clouds are present) because of the different height profiles of water vapour and O₂.

In order to remove this limitation, a new water vapor retrieval is developed. It is based on a look-up-table approach, which intrinsically corrects for the influence of ground albedo and clouds. It will also intrinsically account for saturation effects due to radiative transfer as well as the instrumental resolution, since the calculations will be done at high spectral resolution. As it is not based on the O₂ absorption in order to obtain measured air mass factors, it can be applied to other spectral regions in the visible spectral range. It is therefore also applicable to other satellite sensors, which do not cover the red spectral range. First results and comparisons to the operational product will be presented.

According to recent research, black carbon has the second strongest effect on the earth climate system after carbon dioxide. In high Northern latitudes, industrial gas flares are an important source of black carbon, especially in winter. This fact is particularly relevant for the relatively fast observed climate change in the Arctic since deposition of black carbon changes the albedo of snow and ice, thus leading to a positive feedback cycle.

Here we explore gas flare detection and Fire Radiative Power (FRP) retrievals of the German FireBird TET-1 and BIRD Hotspot Recognition Systems (HSRS), the VIIRS sensor on board of the S-NPP satellite, and the MODIS sensor using temporally close to near coincident data acquisitions. Comparison is based on level 2 products developed for fire detection for the different sensors; in the case of S-NPP VIIRS we use two products: the new VIIRS 750m algorithm based on MODIS collection 6, and the 350 m algorithm based on the VIIRS mid-infrared I (Imaging) band, which offers high resolution, but no FRP retrievals.

Results indicate that the highest resolution FireBird sensors offer the best detection capacities, though the level two product shows false alarms, followed by the VIIRS 350 m and 750 m algorithms. MODIS has the lowest detection rate. Preliminary results of FRP retrievals show that FireBird and VIIRS algorithms have a good agreement. Given the fact that most gas flaring is at the detection limit by medium to coarse resolution space borne sensors – and hence measurement errors may be high -  our results indicates that a quantitative evaluation of gas flaring using these sensors is feasible. Results shall be used to develop a gas flare detection algorithm for Sentinel-3, and a similar methodology wil be employed to validate the capacity of Sentine 3 to detect and characterize small high temperature sources such a s gas flares.

Information on greenhouse gas (GHG) concentrations can be obtained from satellites by observing the spectrum of backscattered and reflected solar irradiance. Within ESA’s GHG-CCI project, several different retrieval algorithms have been applied to generate long time series of column-averaged dry-air mole fractions of carbon dioxide (XCO₂) and methane (XCH₄) based on observations from the SCIAMACHY and the GOSAT-TANSO instrument.

Several components of the complex retrieval procedure can lead to systematic biases, including errors in spectroscopy, instrumental problems, and errors introduced by the radiative transfer calculation. The latter may be caused, for example, by incomplete knowledge or unconsidered effects of thin cirrus clouds and aerosols on the path of light through the atmosphere. Although retrieval algorithms have significantly improved in recent years, particularly with the development of “full physics” algorithms, aerosols and thin cirrus clouds are still thought to be major drivers of systematic errors.

In this study we investigate to what extent differences between the different GHG-CCI products and independent GHG observations can be attributed to the presence of aerosols. For this purpose we analyse correlations between these differences and aerosol measurements from ground-based networks and satellites. In particular, we analyse differences between the GHG-CCI products and collocated measurements of XCO₂ and XCH₄ from the Total Carbon Column Observing Network (TCCON) in combination with aerosol measurements from the Aerosol Robotic Network (AERONET). Similarly, we use satellite-based aerosol observations from multiple instruments, e.g. the AATSR provided through the Aerosol-CCI project, to investigate the influence of aerosols on retrieval biases. Not only the influence of aerosol optical depth but also of other aerosol parameters such as aerosol size and type are considered.

The tropospheric column abundance of nitrogen dioxide (NO₂) has been measured from space continuously since the pioneering mission GOME was launched in 1995 on board of ESA’s ERS-2 mid-morning polar-orbiting satellite: by the mid-afternoon mission EOS-Aura OMI (since 2004) and by the mid-morning missions Envisat SCIAMACHY (2002-2012) and GOME-2 on board of MetOp-A (since 2006) and MetOp-B (since 2012). In the context of the Copernicus Earth observation programme, tropospheric NO₂ will be further monitored by the upcoming afternoon Sentinel-5p TROPOMI, the geostationary MTG-S Sentinel-4, and the mid-morning MetOp-SG Sentinel-5 missions. The validation, intercomparison, and merging of these historical and new datasets, acquired at different solar local times, require advanced co-location methods and ad hoc correction schemes. Indeed, both tropospheric and stratospheric NO₂ concentrations exhibit a pronounced diurnal cycle, primarily of photochemical origin and related to the varying solar zenith angle (SZA), but also affected by intra-day variability of anthropogenic and biogenic emissions. In support to validation and exploitation activities, we present an in-depth characterization of the diurnal cycle of NO₂ and related species, and discuss its impact on data intercomparisons.

To this purpose, the tropospheric distribution of nitrogen oxides has been evaluated on a global 2x2.5 degree grid and at high temporal resolution with the IMAGESv2 Chemical Transport Model (CTM). Using ERA-interim meteorological fields and extensive emission inventories, IMAGES calculates the evolution of 117 chemical compounds. The model output is used to identify distinct regimes, and for each such regime the impact due to differences in solar local time for the above-mentioned missions is quantified. These regimes are mainly related to differences in SZA variation, e.g. polar day versus an equatorial day, and differences in emissions, e.g., forest fires versus urban pollution. To assess the validity of the modeled diurnal cycles, they are compared to ground-based MAX-DOAS UV-visible measurements at selected sites representing both rural and urban conditions.

Since tropospheric NO₂ column data are derived from satellite radiance measurements by subtracting from the total NO₂ column an estimate of the stratospheric column, also the stratospheric diurnal cycle must be understood and taken into account. Using a parameterization of PSCBOX/SLIMCAT modeling results, differences between level-3 gridded monthly-mean stratospheric columns from GOME-2A, GOME-2B, and OMI are simulated. It is shown that even when nominal overpass times are similar (GOME-2A vs. GOME-2B), slight differences in actual SZA can still lead to discernible NO₂ differences. Correction schemes based on model simulations are shown to be a valuable tool in reducing comparison errors due to different overpass times.

Finally, prospects for the validation and exploitation of future geostationary and sun-synchronous missions are discussed.

Due to similarities in their radiometric signatures, it is rarely possible to retrieve aerosol and cloud properties simultaneously from passive satellite imagery. A plethora of filtering techniques have been developed to ensure aerosol and cloud are analysed separately, which neglects interesting regions of interaction between the two and limits the spatial coverage of such products.

The Optimal Retrieval of Aerosol and Cloud (ORAC) is a single algorithm that can retrieve both the aerosol and cloud properties consistent with a single measurement. Various metrics to identify acceptable fits to observations are provided, through which an a posteriori classification of particle type can be obtained. The ensemble of retrievals produced allows investigation of the effects of contamination and data coverage on existing products and a potential window to study aerosol-cloud interactions. Initial results produced within the aerosol_cci and cloud_cci projects (supported by an ESA Living Planet Fellowship) will be presented, highlighting the utility of ensemble retrieval products in poorly constrained analyses.

The determination of trace gases such as NO2, HCHO or SO2 daily cycle from Sentinel-4 observations requires an accurate knowledge of the surface BRF as surface reflectance magnitude is undergoing important changes during the course of the day. These changes result from changing illumination conditions at each S4 acquisition cycle. The shape of these daily variations depends both on surface radiative properties and illumination conditions that are controlled by the sun position and the amount of sky radiation around this position. Scattering optical thickness, which changes with wavelength, determines the intensity of sky radiation. It is therefore expected that the way sky radiation shapes the surface BRF is also wavelength-dependent.

 

Coupling surface BRF with atmospheric scattering in Radiative Transfer Model (RTM) is pretty CPU intensive. Consequently, a Lambertian Equivalent Reflectance (LER) is a convenient assumption to circumvent the amount of computer time that should be dedicated to this coupling, a criteria particularly relevant for the near real-time processing of S4 data. However, there is no unique way to estimate the LER when surface BRF is known.

 

The current work illustrates the way sky radiation shapes the surface BRF for typical S4 observation and illumination geometries as a function of the wavelength and scattering optical thickness. These results will be used to demonstrate the impact of LER / surface BRF relationships on the accuracy of radiations transfer in the various S4 spectral regions. Finally, the implications of such simplification on possible systematic bias on daily cycle retrieval as a function of wavelength will be discussed. 

Carbon monoxide and methane are key species of Earth's atmosphere.
Accordingly, a large number of spaceborne sensors are observing these species in the microwave, thermal and near infrared.
For the analysis of short wave infrared spectra measured by SCIAMACHY and similar instrument(s) we had developed the Beer InfraRed Retrieval Algorithm:
BIRRA is a separable least squares fit of the measured radiance with respect to molecular column densities and auxiliary parameters
(optional: surface albedo, baseline, slit function width, and wavenumber shift).
BIRRA has been implemented in the operational SCIAMACHY L1 to 2 processor for the retrieval of CO and CH₄ from channel 8 and 6, respectively;
the validation study reported here is based on the BIRRA prototype version giving more flexibility.

Verification and validation are critical elements of any code development, and mandatory for the assessment of spaceborne remote sensing products.
In this poster we report on intercomparisons of CO columns estimated from SCIAMACHY with coincident and co-located retrievals provided by ground-based Fourier transform infrared spectroscopy.
More specifically, we have used data from several NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) stations.
Like SCIAMACHY's channel 8 the TCCON instruments utilize the 2.3 µm band of carbon monoxide, whereas NDACC observes the CO mid infrared absorption.
The results indicate the importance of appropriate sampling areas around the station, calibration of level 1 data, and post-processing of the BIRRA retrievals (esp. filtering).
Furthermore, the increased deviations of the spaceborne and ground-based columns in the later years of the mission clearly demonstrate the impact of the degrading channel 8 detector.
Overall, the CO columns derived from SCIAMACHY and NDACC/TCCON largely agree within the error bars.

The ionosphere is highly variable atmospheric plasma influenced from below and from above. The most important factors that affect ionosphere are related solar and geomagnetic activity (e.g. solar irradiation, solar wind coupling, through the magnetosphere). Ionosphere reflects external forcing in variability on scales from minutes to a solar cycle. However, the influence of the lower atmosphere through thermospheric chemistry and dynamics cannot be neglected because the number of neutrals highly exceeds the ionized particles by several orders of magnitude. Processes in the lower laying atmosphere such as UV radiation absorption by the stratospheric ozone, severe meteorological formations in the troposphere, and/or generation of wide range of atmospheric waves by the orography, earthquakes, waves related to human activity and other sources, play an important role in the ionospheric behavior. As the atmospheric waves propagate upward from the source region, their energy tends to be conserved and consequently the amplitude grows due to the decreasing atmospheric density. Despite the fact that most of the atmospheric waves do not reach higher heights than mesosphere and lower thermosphere an important portion of
the atmospheric waves penetrate up to the ionosphere-thermosphere system,
which has been proven experimentally. Signatures of the wave-like oscillation within ionospheric plasma are detected by mean of vertical sounding data (electron density and plasma drift). The data are joined with stratospheric measurements and tropospheric observations to demonstrate the atmospheric vertical influence.

Environmental pollution is one of the current interest ecology problems. Propagation of pollutants from pipes of industrial enterprises and public transport contributes to overall pollution of various regions. During the summer period (or periods of drought) forest and peat fires of significant intensities may also arise (e.g. summer 2010 in the European part of Russia). Amount of public resources needed to eliminate pollution, to secure people health and to provide safety of different ecosystems depend on effectiveness and timing of solving the problem.

In the previous works of the author (Novikov, 2013, 2015) the optimization problem of a mean pollution concentration in region from local sources (viz. regions of industrial pipes or fires) was considered and one-step algorithm for solving the problem was proposed. As a mathematical model of pollution propagation the three-dimensional non-stationary convection-diffusion equation was used. Tikhonov regularization (Tikhonov et al., 1979) was applied to solve the studied problem as it is ill-posed. The algorithm of solution was built on the basis of ''dual'' representation of residual functional (Marchuk et al., 1982), adjoint equations methods (Marchuk, 2000) and optimal control methods (Agoshkov, 2003).

However, in the previous papers an initial mean pollution concentration (i.e. mean concentration calculated without ''controls'') or an initial economic damage may be reduced insignificantly (for example, only fourfold). Attempt to reduce the initial concentration more significant may lead to nonphysical solution of the problem (for example to negative pollution concentration, Novikov, 2013). In the cited work the initial mean concentration was reduced tenfold, but the concentration of impurities (the solution of pollution spread problem) was negative in some areas of the region of interest. Such the effects took place because the "controls" (the laws, utilizing which it is necessary to reduce intensities of local sources) was calculated in one step by using the explicit formula. Application of multi-step algorithm proposed in this work yields decrease in the initial mean pollution concentration up to level determined by sanitary standards, i.e. a tenfold reduction or more. “Controls” are repeatedly evaluated and refined in the method. Moreover, the algorithm does not lead to nonphysical solutions of the problem. Results of numerical experiments (in Moscow region as an example) illustrating theoretical statements of the studied problem and effectiveness of the proposed algorithm are also demonstrated.

The work was supported by Russian Science Foundation (project 14-11-00609).

The TROPOMI instrument on board of the Copernicus Sentinel-5 Precursor satellite will deliver a suite of geophysical data products providing information on air quality and climate. Here, the operational processing within the Copernicus ground system does not cover all possible data products, which gives unique and exciting opportunities for scientific data exploitation. In this presentation, we will discuss the non-operational processing of the isotopic composition of water vapor and solar-induced fluorescence from TROPOMI observations. HDO and H₂O water vapor column densities are inferred from TROPOMI SWIR measurements at 2.3 μm, which will provide valuable information on the hydrological cycle. The solar induced fluorescence are retrieved from Fraunhofer lines close to the O₂ A absorption band of atmospheric oxygen. This data product will improve our knowledge about the photosynthetic functioning of terrestrial ecosystems. Using the SICOR retrieval package, we will present a performance analysis based on simulated TROPOMI measurements over the US and discuss the algorithm maturity showing applications to GOME-2, GOSAT and SCIAMACHY measurements. 

Atmospheric aerosols affect the global climate by scattering and absorbing solar radiation and thereby cool or heat the earth atmospheric system. On a global average the cooling effect due to aerosol scattering predominates the total direct aerosol effect. But some types of atmospheric aerosols like black carbon from fossil fuel burning or dessert dust absorb solar radiation and lead to local atmospheric warming. This local warming of atmospheric layers can hinder cloud formation and so reinforce the warming effect of absorbing aerosols. Even if this warming is overwhelmed by the cooling effect it is necessary to improve our knowledge on the global distribution of absorbing aerosols if we want to understand and predict local climate variations. From satellite measurement commonly retrieved Aerosol Optical Depth (AOD), which is the vertical integrated aerosol volume extinction, gives no information on the absorbing or scattering quantities of the observed aerosol. To distinct absorption from scattering independent measurements at different viewing geometries are needed. Furthermore the reflection properties of the underlying surface has to be known and therewith distinct absorption from scattering. Dual view sensors like the AATSR in the past or SLSTR in future provide such information in regions where either one of the two views is sun glint effected the other is not. Within the 1^st phase of the ESA CCI-Aerosol project we developed an innovative retrieval method to quantify aerosol absorption over the ocean. In this approach the sun glint is determined by utilizing the information in the thermal AATSR channels at 12, 10.8 and 3.7 μm and further used as the lower boundary condition to determine AOD and SSA simultaneously for 3 channels in the visible and near infrared and 2 viewing geometries. For the sun glint it was found that under a specific observation geometry no unique solution exits. This ambiguity made a clear distinction of the contribution from sun glint and aerosol reflectance to the TOA signal impossible. By moving the development further forward a decision was made to change the core part of the retrieval approach to an optimal estimation scheme where surface reflectance, AOD and SSA is retrieved simultaneously. As forward model pre calculated 8 dimensional LUTs are used. The first 5 dimensions are related to observation geometry, surface pressure and refractive index. The refractive index of the water body was calculated with respect to sea surface temperature und salinity. Those parameters are fixed during the optimal estimation process and obtained from ECMWF reanalyze data and satellite observing geometry. Reflection properties for the wind roughed sea surface are based on Cox and Munk where a linear relation between the 10m wind speed and surface reflection was found. The 3 retrieved quantities in the state vector are AOD, SSA and effective wind speed. Prior information for AOD is obtained from reanalyze MACC data set, SSA prior is taken from the AeroCom and wind speed information is taken from ECMWF reanalyze data. The retrieval shows better results in those areas where we see an ambiguity in the relation of wind speed and surface reflection. A further advantage of the new method is an increase in computation speed, which provides us with the ability to generate a bigger dataset for validation purposes. The presentation will focus on the major improvements of the algorithm and results of comparison studies with ground based and other satellites aerosol absorption products.

The Airborne ROmanian Measurements of Aerosols and Trace gases campaign (AROMAT 2015) took place during the last two weeks of August 2015. The campaign was held in the framework of the ESA Copernicus Earth observation programme aiming to test recently developed airborne observation systems dedicated to satellite validation activities and to setup the technological background of future ESA Atmospheric Sentinels. The campaign was coordinated by the Belgian Institute for Space Aeronomy (BIRA) and included several European research institutions from Romania, Germany, the Netherlands and Norway.

AROMAT 2015 was focused on simultaneous ground based and air-born measurements of aerosols and SO₂ / NO_x / H₂CO. The equipments used during AROMAT 2015 cover a wide range of techniques from in-situ to passive and active remote sensing (http://uv-vis.aeronomie.be/aromat). The measurements were performed in two significant sites: Bucharest - the biggest city in Romania, where anthropogenic activities are dominant and Turceni - a large power plant located in a rural area in the Tg. Jiu Valley.

The Bucharest phase of AROMAT was focused on ground-based mobile passive remote sensing and airborne measurements, covering SO₂, NO₂ and the aerosol component. In addition, active remote sensing measurements were performed to assess the aerosol vertical structure and its optical properties. Simultaneously, the Turceni phase covered a more complex scenario: several passive UV SO₂ cameras were designed to perform tomography measurements of the power plant plume, the scanning UV depolarization lidar performed scans within the plume and the in-situ instruments were continuously monitoring the air-quality at ground level. In parallel, two airborne platforms were designed to perform a complete mapping of the area.

The aim of the paper is to present results of the AROMAT 2015 campaign from the ground based perspective, focused on air quality in-situ SO₂, NO_x and aerosols. For Bucharest, lidar data shows the evolution of the planetary boundary layer (PBL) and the associated optical parameters throughout the measured period. Optical parameters combined with AERONET sun-photometer data are used to analyze the presence of several aerosol layers for the case studies. Several aerosol layers from long range transport of biomass burning have been detected above the PBL. For Turceni, lidar data indicates that the plume is confined in the PBL. The in-situ gas analyzers detected several cases of high concentration for SO₂, NO and NO_x at ground level. Even if the pollutant levels were lower than the limits given by national and EU air quality regulations, the instruments performance has been tested close to a significant SO₂ / NO₂ source. Several UV SO2 cameras were located around the Turceni power plant in a first demonstration experiment to obtain tomography measurements of SO2 emissions. The plume path concentration has been assessed from a three point perspective. The studies were used to assess the evolution of the plume within the PBL. All results will be further used in the preparation of TROPOMI/S5p validation campaign foreseen for 2017 in Romania.

The evaluation of the surface UV irradiances derived from the Ozone Monitoring Instrument (OMI) onboard the AURA satellite with a Norwegian Institute for Air Research UV multi-filter actinometer (NILU-UV) situated in the Laboratory of Atmospheric Physics (LAP) in the Aristotle University of Thessaloniki (40.69°N, 22.96°E) is presented in this study. The NILU-UV instrument provides irradiance measurements at one-minute intervals and at five wavelength bands with nominal wavelengths at 302, 312, 320, 340 and 380 nm and a full width at half maximum, FWHM, of approximately 10 nm.  For the calibration of the NILU-UV data collocated and synchronous spectra resulting from a Brewer MKII spectrophotometer, also operating on site, were used. NILU-UV optical characterisations at the LAP’s optical unit revealed small deviations from the nominal wavelengths, while the cosine and azimuthal errors were quantified and implicitly taken into account through the calibration procedures.

The NILU-UV QA/QC data have been compared with the OMI/Aura overpass and local noon irradiances at 305, 310, 324 and 380 nm for a 10 year period over Thessaloniki between 2005 and 2014. The effects of wavelength differences, temporal averaging, spatial co-location criteria, choice of pixel level quality flags and cloudiness have been examined at length. The satellite overpass UV irradiance at 305nm compared to the NILU 302nm irradiance provides r-squared correlations ranging between 0.91 and 0.96 depending on the choice of spatial collocation [between 10, 25 and 50km radius], temporal averaging [either ±5min or ±30min] and cloudiness restrictions [either from the satellite product and/or ground-based cloud flagging.] For local noon UV irradiance comparisons in those wavelengths the correlations remain the same. When examining the 380nm/376nm pair, the comparisons show some differentiation: the local noon UV comparisons, using a strict and dual cloudiness flag, produce the highest r-squared values of around 0.92. However, both the overpass and the local noon un-cloudiness flagged comparisons for that wavelength pair show the worse correlations between 0.70 and 0.88 depending on the rest of the spatiotemporal choices.

Overall, the findings of the validation of the OMI/Aura UVB irradiances product using the NILU-UV actinometer in Thessaloniki, Greece, are well in-line with similar studies, providing useful information and pointers for the further improvement of the nominal satellite retrieval algorithm.

The Earth Explorer Missions of ESA’s Living Planet Programme serve as demonstrators for innovative new technology in space, while additionally fulfilling requirements of different user communities, which have not been met before. The novelty of the concepts, type of measurements, and new resulting products demand a special focus on new procedures for calibration and product validation (Cal/Val) during the operational phase of the mission to meet the requirements. 

ADM-Aeolus is an upcoming Earth Explorer mission, carrying the first Doppler wind lidar in space (ALADIN) to observe global wind profile measurements. These profiles will be provided in near real time to improve Numerical Weather Prediction. In addition, spin-off products will provide vertically resolved co-polar backscatter and extinction coefficients.

An ADM-Aeolus Science and Cal/Val workshop at ESA-ESRIN in Frascati (Italy) in February 2015 brought together the involved user-community, industry and ESA to provide a platform for presentations and discussion on the science, instrument, and product status as well as on the Cal/Val proposals. While currently airborne campaigns and simulation studies give important support in the launch preparation phase, a wide field of additional Cal/Val activities will be in place after launch in the commissioning and operational mission phases (scheduled for 2017).

An overview of the Cal/Val related activities planned for the in-orbit phase as well as the methods and procedures to assure that the end-users are provided with well documented and fully characterized instrument data and products from ESA side will be presented. 

The aim of the Gap Analysis for Integrated Atmospheric ECV CLImate Monitoring (GAIA-CLIM) project is to improve our ability to use ground-based and sub-orbital observations to characterise satellite observations for a number of atmospheric Essential Climate Variables (ECVs). The key outcomes will be a “Virtual Observatory” facility of co-locations and their uncertainties and a report on gaps in capabilities or understanding.

This presentation will provide an overview of project objectives that include mapping capabilities, improving non-satellite measurement characterization, accounting for measurement mismatches and the use of data assimilation. Some initial results and outcomes shall be highlighted to give a flavour of what may be expected in the future. If successful, GAIA-CLIM is foreseen as contributing to the forthcoming Copernicus services. Suggested interactions with ESA, and especially with the CCI projects, are very welcome.

The Volcanic Plume Removal (VPR) is a simple and fast procedure developed to retrieve, the aerosol optical depth, effective radii and total column abundance (mass) and the sulphur dioxide (SO₂) mass contained in a tropospheric volcanic cloud from the thermal radiances at 8.7, 11 and 12 mm. As well known, the detection and retrievals of volcanic clouds is important because of their effects on the environment, climate, public health and in particular to aviation security. The VPR is based on the estimation of a virtual image representing what the sensor would have seen in a multispectral thermal image if the volcanic cloud were not present. To run the VPR a set of parameters, related to the volcanic area, particles type and sensor, is required. This set is previously calculated using MODTRAN radiative transfer model. Currently the only input data is the air temperature at the mean plume altitude. The first version of VPR [1-2] has been already tested for both MODIS and SEVIRI sensors for only pumice ash type. In this work the improved version of the VPR [3], prepared for MSG-SEVIRI (Meteosat Second Generation — Spinning Enhanced Visible and Infra Red Imager) that includes various type of volcanic particles (pumice, andesite, obsidian, ice, water and sulphuric acid droplets) is described. As a multi-particles test case the results of the VPR, applied to a recent eruption of Mt. Etna (Italy, Sicily) in which both ash and ice particles and SO₂ are present in the volcanic cloud, are here presented. The volcanic ice/ash discrimination is performed using the Brightness Temperature Difference (BTD) technique. From the columnar ash mass, by knowing the volcanic cloud thickness, it is possible to retrieve the ash concentration that is an important parameter for the air traffic definition of the No Fly and Enhanced Procedures Zone (NFZ and EPZ) in case of volcanic eruption.

REFERENCES

[1] Pugnaghi, S., Guerrieri, L., Corradini, S., Merucci, L., and Arvani, B.: A new simplified approach for simultaneous retrieval of SO2 and ash content of tropospheric volcanic clouds: an application to the Mt Etna volcano, Atmos. Meas. Tech., 6, 1315-1327, doi:10.5194/amt-6-1315-2013, 2013.

[2] Guerrieri, L., Merucci, L., Corradini, S., and Pugnaghi, S.: Evolution of the 2011 Mt. Etna ash and SO2 lava fountain episodes using SEVIRI data and VPR retrieval approach, Journal of Volcanology and Geothermal Research, Vol. 291, pp. 63-71, doi:10.1016/j.jvolgeores.2014.12.016, 2015.

[3] Pugnaghi, S., Guerrieri, L., Corradini, S., Merucci, L., Improvements of the volcanic plume removal (VPR) approach for the real-time ash and SO2 satellite retrievals, in preparation.

A large variety of water vapour data records is available to date. Without proper background information and understanding of the limitations of available data records, these data may be incorrectly utilised or misinterpreted. The overall goal of assessments of CDRs is to conduct objective and independent evaluations and inter-comparisons in order to point out strengths, differences and limitations and, if possible, to provide reasons for them. The need for such assessments is part of the GCOS guidelines for the generation of data products. The GEWEX Data and Assessments Panel (GDAP) has initiated the GEWEX water vapor assessment (G-VAP) which has the major purpose to quantify the current state of the art in water vapour products (upper tropospheric humidity, specific humidity and temperature profiles as well as total column water vapour) being constructed for climate applications. In order to support GDAP and the general climate analysis community G-VAP intends to answer, among others, the following questions:

a) How large are the differences in observed temporal changes in long-term satellite data records of water vapour on global and regional scales?

b) Are the differences in observed temporal changes within uncertainty limits?

c) What is the degree of homogeneity (break points) of each long-term satellite data record?

A general overview of G-VAP will be given. The focus of the presentation will be on observed inconsistencies among the long-term satellite data records as observed by inter-comparisons and comparison to in-situ observations and the stability analysis. On basis of consistently applied tools major differences in state-of-art CDRs have been identified, documented and to a large extend explained. Also, the science questions given in the introduction have largely been answered. The results and the answers for TCWV are summarized as follows: On global ice free ocean scale the trend estimates among six long-term data records were generally found to be significantly different. Maxima in standard deviation among the data records are found over, e.g., tropical rain forests. These and other noticeable regions coincide with maxima in mean absolute differences among trend estimates. These distinct features can be explained with break points which manifest on regional scale only and which do not appear in stability analysis relative to ground-based observations. Results from inter-comparisons of zonal means from more than 20 data records and from profile inter-comparisons will also been shown.

The European Space Agency (ESA) has selected the EarthCARE satellite mission for implementation as its sixth Earth Explorer Mission, in cooperation with the Japan Aerospace Exploration Agency (JAXA). The satellite payload consists of the Atmospheric Lidar (ATLID) , the Japanese Cloud Profiling Radar (CPR), the Multi-Spectral Imager (MSI)  and the Broad-Band Radiometer (BBR), and provides a suite of cloud, aerosol and radiation products, several of which are combining data from multiple instruments. The unique synergistic nature of this mission poses challenges for geophysical product validation, in particular for cloud products and products. To gather contributions from numerous providers of correlative instruments and analysis teams, ESA will issue an Announcement of Opportunity dedicated to the Geophysical Validation of EarthCARE in the course of 2016. The call will solicit contributions for the validation of Level 1B products (particularly challenging for EarthCARE’s active instruments) and Instrument-specific and synergetic level 2 Products, and also of auxiliary parameters that help characterise algorithm deviations. The groups that have submitted accepted proposals will be united in the EarthCARE validation team and provided with access to preliminary EarthCARE data already during the Commissioning Phase, and will further benefit from interaction with algorithm developers. Coordination of efforts will be important in view of the resource-intensive campaigns needed for the validation of the product suite of EarthCARE.  Reciprocally, the rapid delivery of preliminary correlative data to ESA is an important prerequisite for the Commissioning Phase, to allow an initial assessment by the end of that phase. Support tools will be described that facilitate correlative observation planning and intercomparison analysis.

Airborne hyperspectral measurements are one major tool to validate cloud optical products derived with satellite instruments. A typical set of optical instruments are ranging from simple RGB- and hyperspectral cameras up to more complicated polarimeters. We will present examples to derive cloud optical thickness and droplet size. In addition we present a new method of boresight calibration using images of the Glory back-scattering ring around the shadow of the observer above clouds. The data were measured with a hyperspectral instrument during an airborne cloud characterization campaign. The measured hyperspectral data were down sized to simulated RGB data similar to human eye sensitive images or aerial cameras. A contrast improved black&white ratio was derived from these RGB data to calculate the difference between the calculated Sun scattering angle relative to the aircraft position and attitude and the center viewing direction of the image of the Glory scattering ring. The optimal angles were found by an iterative method and compared for three different scenes during one mission. The knowledge of these angles and the new method will provide high accuracy measurements and investigations of Sun scattering effects of clouds and aerosols and an easier boresight calibration of imaging cameras. Model calculations are used to find the corresponding cloud optical thickness and droplet size.

Nitrogen dioxide (NO₂) is one of the most prominent air pollutants. Emitted primarily by transport and industry, NO₂ has a major impact on health and economy. In contrast to the very sparse network of air quality monitoring stations, satellite data of NO₂ is ubiquitous and allows for quantifying the NO₂ levels worldwide. However, one drawback of satellite-derived NO₂ products is that they provide solely an estimate of the entire tropospheric column, whereas what is generally needed for air quality applications are the concentrations of NO₂ near the surface.

Here we present a methodology for deriving surface NO₂ concentration fields from OMI and GOME-2A tropospheric column products using the EMEP chemical transport model as auxiliary information. The model is used for providing information of the boundary layer contribution to the total tropospheric column.

For preparation of deriving the surface product, a comprehensive analysis of the spatial and temporal patterns of the NO₂ surface-to-column ratio in Europe was carried out for the year 2011. The results from this analysis indicate that the spatial patterns of the surface-to-column ratio vary only slightly. While the highest ratio values can be found in some shipping lanes, the spatial variability of the ratio in some of the most polluted areas of Europe is not very high. Some but not all urban agglomeration shows high ratios. Focusing on the temporal behavior, the analysis showed that the European-wide average ratio varies between 0.8 μg m^-3 / 10¹⁵ molec. cm^-2 and 1.1 μg m^-3 / 10¹⁵ molec. cm^-2. The ratio increase from January all the way through April when it reaches its maximum, then decreases relatively rapidly to levels of around 0.9 μg m^-3 / 10¹⁵ molec. cm^-2 and then stays mostly constant throughout the summer. The minimum ratio is observed in December.

The knowledge gained from analyzing the spatial and temporal patterns of the surface-to-column ratio was then used to produce surface NO₂ from the daily NO₂ products for OMI and GOME-2A. A validation of the surface NO₂ fields was carried out using station observations of NO₂ as provided by the Airbase database maintained by the European Environment Agency. First results indicate that the methodology is capable of producing surface concentration fields that reproduce the station-observed surface NO₂ levels significantly better than the model surface fields as measured by the root mean squared error.

In addition to deriving satellite-based surface NO₂, we further present initial results of a geostatistical methodology for downscaling satellite products of NO₂ to spatial scales that are more relevant for applications in urban air quality. This is being carried out by applying area-to-point kriging techniques while using high-resolution (1-2 km spatial resolution) runs of a chemical transport model as a spatial proxy.

In combination, these two techniques for deriving surface NO₂ and at the same time downscaling satellite-based NO₂ fields has significant potential for improving monitoring and mapping of regional and local-scale air pollution.

 

The physics of clouds is one of the most important driver of meteorology and the climate system. Apart from this, the distribution of clouds interferes with the majority of satellite measurements techniques. Tropospheric trace gas retrievals are particularly prone to the concentration of clouds within the FOV of the instrument, because already small cloud fractions have the potential to alter the measurement error and significantly increase the uncertainty of the measurement. Hence, the accuracy of tropospheric trace gas retrievals depends on the accuracy of the cloud fraction, particularly for small cloud fractions.

The original IterativeCloud Retrieval Utilities (HICRU) algorithm has been specifically developed for the retrieval of small cloud fractions at high accuracy. This is achieved by inferring a ground albedo map from the dataset itself, minimising the influence of instrument degradation and/or insufficient calibration. HICRU thus requires a minimum of a-priori knowledge. So far, this approach is limited to measurements at sufficiently small viewing angles, such as GOME and SCIAMACHY, for which the use of a single, viewing-angle independent background albedo map is justified.

Here, we demonstrate how this empirical approach may be enhanced so it becomes applicable to satellite instruments like GOME-2, OMI, and the upcoming TROPOMI/S5P with viewing angles up to 45 or even 70 degrees, by parametrising the viewing angle dependence of the TOA reflectance. Furthermore, the enhanced HICRU algorithm comprises an advanced treatment of the temporal evolution using a spatially averaged Fourier series fit. Furthermore,HICRU has the potential to be applied also to instruments with moderate spectral resolution like MERIS, MODIS, or AVHRR as well.

The Microwave Sounder (MWS) will be part of the core payload of the future MetOp-SG operational satellites to be flown as part of the EUMETSAT Polar System-Second Generation (EPS-SG) programme from 2021 onwards. In total there will be six Metop-SG satellites, divided into A and B-series. MWS will be embarked on the A-series together with 3MI, MetImage, IASI-NG, RO and Sentinel-5. The development of MetOp-SG builds upon the experience of the very successful MetOp-A and –B satellites launched in 2006 and 2012, respectively.

MWS is a traditional cross-track scanning microwave radiometer, providing a total number of 24 channels from 23 GHz up to 230 GHz. MWS will sense atmospheric temperature and water vapour profiles and in addition will provide information on cloud liquid water, precipitation, and surface properties. The MWS will contribute to primary mission objectives of the EPS-SG programme in the areas of Numerical Weather Prediction and climate monitoring.

One of the main tasks in the development phase is the preparation of a science plan to detail the scientific work, which is needed to meet the MWS related mission objectives. This plan will be prepared by the MWS Science Advisory Group (SAG) and external experts supported by ESA and EUMETSAT.. The plan  can be used as reference for scientific activities to be undertaken within and outside the MWS SAG in the coming years.

This presentation will start with a short summary of the MWS instrument and requirements and then focus on the science plan content and the schedule of the preparation of this plan. The presentation is intended, on the one hand, to inform the wider science community about potential future science activities and, on the other hand, to encourage the community to make recommendations on any issue needed to be tackled to reach the objectives of the EPS-SG microwave sounding mission.

The Suomi National Polar-orbiting Partnership spacecraft lifted on 28 October 2011, to begin its Earth observation mission. Suomi NPP carries five diverse payload of scientific instruments to monitor the planet, being one of them VIIRS, a scanning radiometer that collects visible and infrared imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. VIIRS data is used to measure cloud and aerosol properties, ocean color, sea and land surface temperature, ice motion and temperature, fires, and Earth's albedo.

 

The Sentinel-5 Precursor mission is intended to provide data continuity for the SCIAMACHY instrument aboard the Envisat satellite and NASA's OMI instrument aboard the Aura satellite. The mission will perform atmospheric monitoring at high temporal and spectral resolution, and increased cloud-free observation. Its payload, the TROPOMI instrument is a UV-VIS-NIR-SWIR push-broom grating spectrometer.

 

Since summer 2013, ESA and the NOAA/NASA SNPP & JPSS have been working together to coordinate an operational concept for the Sentinel-5P mission to fly in ‘loose formation’ with the Suomi-NPP mission. The aim of the constellation is to maximize the science performance by combining VIIRS and TROPOMI data.

 

The highest priority when designing the constellation concept is safety of both satellites, in that respect a similar concept as for the morning and afternoon constellations is proposed for SNPP and S5P, based on control boxes.

 

In order to enhance the science performance the following considerations shall be consider, in order of relevance:

 

1. Minimum distance between both satellites, without compromising their safety.

2. Constant distance between the actual positions of both satellites.

3. Ground track as adjacent as possible, being S5P swath within SNPP swath.

 

The reasoning behind is that quality of the cloud mask degrades sharply when acquisitions are more than 5 minutes apart. Also the sensitivity to the distance variation increases from that number. Finally it would be desirable to observe the clouds from the same angle, so a closer ground-track is desired.

 

The achievement of these three objectives at the same time is not always possible, as the MLST, the Ground-track and the Along-Track separation between satellites are related to each other.. In the case of Sentinel-5P the limiting factor is a constrain in the MLST, that should be higher than 13:30. Suomi-NPP MLST is currently maintained at 13:25, therefore there is 5 minutes difference between both satellites. If the same ground-track is overflown, the along-track separation becomes identical to the MLST difference between both satellites, therefore reducing or increasing the along-track distance between satellites implies a shift on one’s reference ground-track with respect to each other.

 

Both satellites are operated co-ordinately but independently, with the result that the ground-track control of both satellites is not correlated, therefore the spacing between the actual ground-tracks will vary as well as the along-track distance.

 

The proposed paper will quantify the expected along-track distance and the ground-track shift between both satellites, as well as their variations, depending on the safety and MLST constrains. A full overview of the estimated geometry between both satellites acquisitions will be presented.

The monitoring of the state of calibration of any space borne spectrometer is highly important for a number of
trace gas retrievals. Particularly retrievals using absolute radiances are susceptible to changes in the spectral
calibration. One such algorithm is the optimal estimation based retrieval of ozone profiles from UV nadir sun-
normalised radiances. In order to improve the performance the IUP (Institute of Environmental Physics,
Bremen) nadir ozone profile retrieval, which depends primarily on absolute sun-normalised radiances, an
instrument independent soft calibration has been set up at the IUP for GOME (Global Ozone Monitoring
Experiment), SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric ChartographY) and
GOME-2 (Global Ozone Monitoring Experiment-2). This soft calibration can be used to recalibrate radiances
relative to pre-launch as well as calibrating radiances relative to post-launch by using simulated radiances
assuming a reasonable state of the atmosphere and calculating ratios between measured and simulated
radiances. These comparisons are conducted over ocean and land ice using both dark and bright surfaces. The
soft calibration is a useful tool to measure and evaluate degradation and degradation corrections implemented.
As such it is used as a part of the GOME-Evolution project to validate the reprocessed reflectances in the UV
channels 1A (237 nm – 307 nm), 1B (307 nm – 315 nm) and 2 (312 nm – 406).
In addition to an overview of the methodology of the radiance soft calibration its results will be shown GOME,
SCIAMACHY and GOME-2. The effects of the soft calibration on ozone profiles retrieved using the IUP nadir
ozone profile retrieval will be shown in comparisons with ozonesonde and lidar measurements. Furthermore
results from the validation of the newly processed GOME level 1 V5 as well as SCIAMACHY level 1 V8 radiances will be shown.

Within ESA's EarthCARE (Cloud, Aerosol and Radiation Explorer) activity Atmospheric Products from Imager and Lidar (APRIL), the MSI-AER L2a-processor to derive aerosol optical thickness (AOT) above ocean and, where possible, land based on multi-spectral imager (MSI) measurements is developed. MSI is a nadir looking, passive instrument having seven channels in the visible, near-infrared, shortwave- and thermal infrared. It will provide information on aerosols in the arcoss track direction in support to the profile information based on Atmospheric Lidar (ATLID) measurements, which will also, put together, allow for the derivation of a synergy product.

The MSI based AOT retrieval is basically seperated in two different approaches one for ocean and one above land surfaces, only having the atmospheric correction of top of atmosphere reflectances, regarding gaseous absorption and the determination of the Rayleigh path reflectance, in common in a precursory step. While above land prior information of the highly varying surface reflectance is used for the derivation of AOT, above ocean, the surface contribution is simulated using wind speed for parameterizing the sea surface roughness [Cox and Munk, 1954]. Only above ocean, the AOT at 670nm and 865nm will be derived as well as the Angstrom exponent. Above land the AOT is only determined at 670nm. Both retrieval procedures employ the optimal estimation technique (Rodgers, 2000), applying the Gauss-Newton approach for optimisation. This method also enables to derive uncertainty estimates of the retrieved AOT after the final iteration step, which are introduced by errors in the measurment as well as due to uncertainties in prior knowledge. The forward model used in the algorithm relies on pre-calculated look-up tables of modelled radiances of MSI channels at 670nm, 865nm, 1.6μm and 2.1μm. The simulations are carried out by using the radiative transfer code MOMO [Hollstein and Fischer, 2012].

In order to validate the proposed MSI based AOT retrieval procedure before the foreseen start of EarthCARE in 2018, Moderate-resolution Imaging Spectroradiometer (MODIS) measurements as well as EarthCARE simulator (ECSIM) data are used in adapted versions of the M-AOT algorithm. Hence, not only the algorithm itself, but also results based on the MODIS L1b measurements will be presented together with a validation against the MODIS aerosol product.

Cox, C. and Munk, W.: Measurements of the roughness of the sea surface from photographs of the sun's glitter. J.Optical Soc. Amer. 44, Pages 838-850, 1954.

Hollstein, A. and Fischer,J.: Radiative transfer solutions for coupled atmosphere ocean systems using the matrix operator technique. Journal of Quantitative Spectroscopy and Radiative Transfer Volume 113, Issue 7, Pages 536–548, 2012.

Rodgers, C. D., Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific Publishing Co. Ltd., 2000.

Stratospheric profile retrieval of ozone in the Hartley-Huggins band in nadir viewing geometry is one of very few options of obtaining a far-reaching timeseries of ozone profiles. The IUP optimal estimation type retrieval including a spectral soft calibration based on the FUll Retrieval Method (FURM) by Hoogen et al. (1999) has been successfully applied to a number of sensonrs. SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric ChartographY) launched on ENVISAT in March 2002 measures sunlight, transmitted, reflected and scattered by the earth atmosphere or surface (240 nm - 2380 nm) in both nadir and limb viewing geometry. GOME (the Global Ozone Monitoring Instrument) and GOME-2 (the Global Ozone Monitoring Instrument-2) both measure in the nadir geometry from 1995 to 2003 and 2006 to today respectively in the wavelength region between 240 and 790 nm. Validation and intercomparison results for these sensors will be shown. In addition verification results of this algorithm as applied to the TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel 5 precursor will be shown.

[INTRO]. Volcanic ash cloud properties are of significant interest because of their environmental, climatic, and socioeconomic effects. The possibility of monitoring in all weather conditions at a fairly high spatial resolution (less than a few hundreds of meters) and every few minutes after the eruption is the major advantage of using ground-based microwave radar systems. Ground-based weather radar systems usually detect emitted particles by volcanoes (in general referred as tephra) at centimetre scales up to finer sizes of the order of fractions of millimetres. They can also provide data for determining the tephra volume, total mass, and height of eruption clouds (Marzano etal., 2012). Indeed, the radar reflectivity factor is correlated to dielectric and microphysical features of ash particles detected by the scanning beam radar. This allows us to observe and analyse the ash plume temporal variation and dispersal features.

[GOAL]. The eruption in Holuhraun was an effusive eruption with very limited ash generation. However, there were cases where the plume was visible on radar. We'll show a unique case where the same tephra emission at the Holuhraun site has been observed by two different radars: the C-band (5.6 GHz) radar in the east of Iceland (Teigsbjarg) and the mobile polarimetric X-band (9.6 GHz) radar located in south Iceland (Suðurland), at about 90 km and 75 km distance from the eruption, respectively. The dual sighting allows us to obtain more accurate information about the observed event.

[OBSERVATIONS AND CASE STUDY]. We consider all observations of two days, on September 03rd and 04th, 2014, in which both radars detect an evident signature, which is attributable to the volcanic emission. Indeed, on 3rd September, both radars show the main detected ash classes: the Coarse Ash (CA [64-512 μm], more 90 %) and a low presence of Small Lapilli (SL [512-4096 μm], less 10 %) in the selected area near the Holuhraun site. Both radars highlight a plume extension of about 10 km in the afternoon, generated by an ash and gas emission starting from the Holuhraun fissure in the early hours of the morning.

On 4th September, both radars highlight the ash plume above the Holuhraun fissure, also in this case starting for the early hours of the morning. The ash plume identified had an extension of 10-15 km as clearly shown by both radars, and the predominant ash classes identified are the CA and a lower presence of SL.

[BEYOND THE STATE OF THE ART]. Typically, the satellite observations are exploited in monitoring and tracking of ash plume generated by intense eruptive events. Due to their relatively low spatial and temporal resolution and considering the possible blocking of their field of view by meteorological clouds, the usefulness of satellite observations can be limited. However, ground-based microwave radars offer the possibility of monitoring 24 hours a day, in all weather conditions, at a fairly high spatial resolution and every few minutes after an eruption (Marzano etal. , 2012).

[EXPECTED RESULTS AND NEW ASPECTS]. In this presentation, the C-band and X-band observables are analysed during the Holuhraun eruption activity in the beginning of September 2014 and discussed to demonstrate the presence of ash particles emission that where not detected by other more conventional sensors and also supporting the hypothesis of resuspension of deposited tephra at the ground in the area around the site. This work, with the support of other available data, could help the scientists in the study of interaction between the ash and gas emission and the resuspended ash in particular environmental conditions. 

The Payload Data Ground Segment (PDGS) is the component of the overall EarthCARE Ground Segment which receives the science data from the staellite, processes it from level 0 up to level 2B (synergistic products) and makes products available to users. The PDGS is also in charge of instruments calibration and monitoring, products quality control  as well as planning of payload operations.

This poster presents  the functional and architectural breakdown of the PDGS, external interfaces including ECMWF and JAXA. It details the main design drivers including data latency, production model, data volumes and network bandwidth.

The European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) are co-operating to develop the EarthCARE satellite mission with the fundamental objective of improving the understanding of the processes involving clouds, aerosols and radiation in the Earth’s atmosphere.

 

A Cloud Profiling RADAR (CPR), an Atmospheric LIDAR (ATLID), a Broadband Radiometer (BBR) and a Multi-Spectral Imager (MSI), the last being the subject of this paper, constitute the payload complement of the EarthCARE satellite. By acquiring images of the clouds and aerosol distribution, the MSI instrument will provide important contextual information in support of the RADAR and LIDAR data processing.

 

The EarthCARE MSI is relatively compact for a space borne imager. As a consequence, the immediate point-spread function (PSF) of the instrument will be mainly determined by the diffraction caused by the relatively small optical aperture. In order to still achieve a high contrast image, de-convolution processing is applied to remove the impact of diffraction on the PSF. A Lucy-Richardson algorithm has been chosen for this purpose.

 

The correction efficiency of an iterative de-convolution algorithm depends on several performance aspects of the raw data, such as signal-to-noise ratio, spatial sampling frequency and additional variations of the point-spread function due to stray light, motion blur etc. The radiometric quality of the product may also be affected due to de-convolution of noise and processing artefacts at the edges of the field-of-view.

 

The overall impact of the de-convolution is not trivial to assess in an analytical manner. Therefore we have devised an end-to-end test of the instrument and processor performance:

SSTL      and TNO have assembled and tested engineering confidence models (ECM) of      the thermal infrared (TIR) and visible, near infrared & short-wave      infrared (VNS) cameras of the MSI respectively.

Airbus      DS has used a prototype implementation of the EarthCARE ground processor      (ECGP) to process selected test data sets from the ECM characterization      campaigns.

 

The Level 1 data sets generated in this way are representative for the end-to-end chain including the flight hardware and the operational processor, and provide a direct measure of the geometrical and radiometric (SNR, MTF) performance that will be achieved. This paper will describe the system setup and the necessary data pre-processing and post-processing steps applied in order to compare the end-to-end image quality with the L1b performance required by the science community.

Atomic oxygen is one of the key parameters for the photochemistry and energy balance in the upper mesosphere and lower thermosphere (UMLT) region. In situ and remote sensing measurements of atomic oxygen have been conducted by many instruments in the UMLT region. However, atomic oxygen concentrations derived from different measurements are not always consistent with each other. Up to 30% deviations are found for atomic oxygen concentrations derived from SABER (Sounding of the Atmosphere using Broadband Emission Radiometry ) measurements (OH airglow) and SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY ) measurements (O(1S) green line). New atomic oxygen data sets are derived from the nighttime green line emission measurements of the SCIAMACHY instrument based on different photochemical models. They are compared to recently published data sets and we find that the retrieved atomic oxygen concentration depends on the choice of the underlying photochemical model. These dependencies explain a large proportion of the differences between recently published data sets.

Several investigations of solar cycle impact have been performed by monitoring O(1 S) nightglow emissions . However, less investigations were performed on solar cycle variations of atomic oxygen. We performed an analysis on the impact of the 11-year solar cycle on volume emission rates and atomic oxygen abundances for various data sets , with the finding that the solar cycle effect varies with the atomic oxygen data set used . The solar cycle impact on the SCIAMACHY data increases with altitude. Above 96 km, it is significantly larger than predicted by HAMMONIA (Hamburg Model of the Neutral and Ionized Atmosphere). Investigations indicate that these variations are primarily driven by total density compression/expansion variations during the solar cycle, rather than different photolysis rates.

Gravity wave (GW) excitation, propagation, and dissipation is one of the most important coupling mechanisms between the troposphere and the middle atmosphere. Gravity waves couple different atmospheric regions both in the vertical as well as in the horizontal direction by means of momentum and energy transport.

The ESA project GWEX (Gravity Wave EXperiment) will demonstrate the capabilities of infrared limb imaging to detect gravity waves in the lower stratosphere. During this activity, two- and three- dimensional data of temperature and trace species are collected by means of an airborne infrared limb imager. The airborne PREMIER IRLS prototype instrument GLORIA (Gimbaled Limb Observer for Radiance Imaging of the Atmosphere) will be applied on the high-flying German research aircraft HALO in northern Sweden.

GLORIA is a joint development of the Helmholtz Research Facilities Karlsruher Institut für Technologie (KIT) and Forschungszentrum Jülich (FZJ) and combines a classical Fourier Transform Spectrometer with a 2D detector array. GLORIA is a demonstrator for a future infrared limb imager satellite as a follow up concept to PREMIER (EE7 candidate). The capability to image the atmosphere and thereby take several thousand spectra simultaneously improves the spatial sampling of conventional limb sounders by an order of magnitude. Furthermore GLORIA is able to pan the horizontal viewing direction and therefore measure the same volume of air under different angles. Due to these properties tomographic methods can be used to derive 3D temperature and tracer fields with a spatial resolution of better than 30km x 30km x 300m from measurements taken during circular flight patterns. The retrieval algorithm is optimized for large scale 3D retrievals of several hundred thousands of measurements and various atmospheric constituents. It can efficiently solve the problem by quasi-Newton type methods, in our case a truncated conjugate gradient-based trust region scheme.

The Scandinavian mountain range is a source region for gravity waves, which can propagate under appropriate conditions up to the middle atmosphere. Meteorological data is used to forecast gravity wave activity in this region to plan and conduct research flights accordingly. GLORIA will measure spectrally resolved infrared limb images at different viewing directions. These data will be used to reconstruct three dimensional fields of temperature and selected trace species at very high spatial resolution.

By investigating 10 years of January data from ECWMF, we explore the likelihood to find in northern Scandinavia in winter a meteorological situation where GWs are excited in the UTLS and are able to propagate at least up to the mid stratosphere. These data are studied with a wave analysis tool developed for the PREMIER gravity wave study. The results will be coupled to a ray-tracing model to investigate the ability of these waves to propagate to higher altitudes.

We present a new long-term cloud property dataset, covering AVHRR data from 1982-2014, which was produced as part of the ESA Cloud_cci project. The major objectives of the project are (1) to provide cloud climatologies that are consistent in terms of assimilated spectral information content over time, (2) to apply a retrieval algorithm that flexibly accommodates data of different satellite sensors (AVHRR, MODIS, AATSR), (3) to develop and apply a retrieval algorithm utilizing collocated MERIS and AATSR measurements in a synergetic approach, (4) to retrieve all cloud properties simultaneously for radiative consistency with satellite observations, and (5) to produce uncertainty estimates for all retrieved and derived cloud variables. We apply an optimal estimation approach (1D-Var) to fully account for covariance between cloud parameters, satellite observations, and associated uncertainties in a sophisticated way.

We analyse the AVHRR GAC (global area coverage) 33-year cloud property data record for long-term stability in cloud coverage and cloud properties (e.g. effective radius, optical thickness, height) together with a comparison to other existing cloud climatologies. Our analysis comprises a quantification of potentially seen trends and corresponding uncertainty in Cloud_cci data, a discussion of plausible drivers of long-term cloud variability, and a validation against independent data sources (e.g. ground observations and other cloud retrieval algorithms). Moreover, we examine the eligibility of ESA Cloud_cci data for application within climate studies. As an example, our results show that significant correlations exist between monthly tropical cloud coverage (and other cloud properties) and the ENSO index time series. These correlations reproduce expected spatial patterns of ENSO-related spatial variability in cloud coverage. We also show that our algorithm produces consistent cloud property retrievals when either AVHRR or MODIS data are assimilated. This way, Cloud_CCI products combine the benefits of AVHRR GAC long-term temporal coverage at 5km pixel resolution with almost 15 years of MODIS data resolved at 1 km. These are valuable sources for climate model calibration and validation of critical, highly uncertain cloud-related processes.

This contribution focuses on the total and tropospheric NO₂ column products from GOME-2 as developed in the framework of EUMETSAT’s Satellite Application Facility on Atmospheric Composition and UV Radiation (O3M-SAF). Improvements in the NO₂ retrieval algorithm will be presented , and we show examples of air quality applications with GOME-2 NO₂ data.

Total NO₂ columns from GOME-2 are retrieved with the Differential Optical Absorption Spectroscopy (DOAS) method using the large 425-497 nm wavelength fitting window in order to increase the signal to noise ratio. The tropospheric NO₂ column is derived using an improved Stratospheric-Tropospheric separation (STS) algorithm, followed by an air mass factor conversion calculated with the LIDORT model. For the calculation of the tropospheric AMF, a new surface albedo (LER) climatology based on GOME-2 observations for 2007-2013 is used.

We present intercomparisons of the GOME-2 NO₂ measurements from MetOp-A and B, and comparisons with other NO₂ satellite products. Furthermore, the use of GOME-2 tropospheric NO₂ data for air quality applications will be illustrated for China and Europe, and time-series of tropospheric NO₂ are analyzed to investigate trends in air pollutants.

The Arctic is warming faster than the global average especially in autumn and winter and substantial reductions in summer and winter sea-ice have been observed recently. It is also the part of the globe where climate model scenarios show the largest spread. The impact of clouds on sea ice and Arctic amplification is not well understood even though an increase in clouds in winter is expected to have a warming effect due to the initial small amounts of cloud condensate and especially in liquid form. Many recent observational data sets report significant amounts of mixed-phase clouds over the Arctic in all seasons. The frequent occurrence of Arctic mixed-phase clouds has important implications for the cloud radiative forcing at the surface, since mixed-phase clouds tend to be optically thicker than ice-only clouds and emit more downward long-wave flux which increases the surface temperature and sea-ice melt. A number of studies have shown that models underestimate the amount of cloud water in Arctic mixed-phase clouds. In this study we investigate how cloudiness affect the Arctic warming and sea-ice retreat in the global coupled climate model EC-Earth for AMIP and transient experiments. We also investigate how the cloud-radiation and sea-ice interactions affect the circulation in EC-Earth and in ERA-Interim reanalysis data and compare to ESA Cloud_cci and sea-ice_cci and other observational data sets.

Satellite observations provide a continuous survey of the atmosphere over the whole globe. IR sounders have been observing our planet since 1979. The spectral resolution has improved from TIROS-N Operational Vertical Sounders (TOVS) to the Atmospheric InfraRed Sounder (AIRS), and to the InfraRed Atmospheric Sounding Interferometer (IASI); resolution within the CO₂ absorption band makes these passive sounders most sensitive to semi-transparent cirrus, day and night. These clouds cover about 30% of the globe and play an important role in the climate system, especially on the heating of the upper troposphere.

The CIRS-LMD cloud property retrieval approach is based on a weighted χ² method and uses the channels around the 15 μm CO₂ absorption band [Stubenrauch et al., 2010 and references therein], providing cloud pressure and emissivity of a single cloud layer (which is the uppermost one in the case of multi-layer clouds). The accuracy of the CIRS-LMD cloud retrieval has been estimated using the information from active sounders: the AIRS instrument is a part of the NASA Afternoon Constellation (A-Train) mission, which includes a two-wavelength polarization-sensitive nadir viewing lidar, providing high-resolution vertical profiles of aerosols and clouds.

Once the cloud physical properties (cloud pressure and IR emissivity) are retrieved, cirrus bulk microphysical properties (De and IWP) are determined from spectral emissivity differences between 8 and 12 mm [Guignard et al. 2012]. The emissivities are determined using the retrieved cloud pressure and are then compared to those simulated by the radiative transfer model. We use the latest version of the radiative transfer model 4A (http://4aop.noveltis.com), which has been coupled with the DISORT algorithm to take into account multiple scattering of ice crystals. The code incorporates single scattering properties of column-like or aggregate-like ice crystals provided by MetOffice [Baran et al., 2001; Baran and Francis, 2004].

We present cloud properties retrieved from IASI and AIRS observations (9:30 AM and 9:30 PM and 1:30 AM and 1:30 PM local time, respectively) in relation to their atmospheric environment. The IASI cloud data are part of the ESA Cloud CCI project, and our results will be compared to those of other ESA Cloud CCI data sets.

 

References:

Baran, A.J. and Francis, P.N. and Havemann, S. and Yang, P: A study of the absorption and extinction properties of hexagonal ice columns and plates in random and preferred orientation, using exact T-matrix theory and aircraft observations of cirrus, J. Quant. Spectrosc. Ra., 70, 505–518, 2001.

Baran, A. J. and Francis, P. N.: On the radiative properties of cirrus cloud at solar and thermal wavelengths: A test of model consistency using high-resolution airborne radiance measurements, Q. J. Roy. Meteor. Soc.,130, 763-778, 2004.

Guignard, A., C. J. Stubenrauch, A. J.Baran, and R. Armante., Bulk microphysical properties of semi-transparent cirrus from AIRS: a six year global climatology and statistical analysis in synergy with geometrical profiling data from CloudSat-CALIPSO, Atmos, Chem. Phys., 12, 503-525, 2012.

Stubenrauch, C.J., S. Cros, A. Guignard, and N. Lamquin, A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat, Atmos. Chem. Phys., 10, 7197–7214, 2010.

Nitrogen dioxide (NO2) and sulphur dioxide (SO2) are known as toxic atmospheric trace gases. Both trace gases have a negative effect on the atmospheric chemistry, human health and environment. "Acid rain" being one of the most popular negative effect of these trace gases.

Measurements of NO2 and SO2, including also other trace gases, were done during two major campaigns for atmospheric research and data satellite validation in Romania. The campaigns were named "Airborne ROmanian Measurements of Aerosols and Trace gases" (AROMAT 1&2). The AROMAT campaigns were performed during two weeks in 2014, respectively 2015.

The AROMAT campaigns took place in the framework of the European Space Agency (ESA) Copernicus Earth Observation Programme and were conducted by several universities and research institutions from Romania (UGAL, INOE, INCAS) and Europe (BIRA-IASB Belgium, MPI-C Germany, FUB Germany, KNMI Netherlands, and Uni.Bremen Germany). Dunarea de Jos University of Galati contributed to these campaigns with Car-DOAS (Differential Optical Absorption Spectroscopy) measurements and aircraft-based DOAS observations using an Ultralight Motorized (ULM) aircraft. The AROMAT campaigns had the aim to prepare the validation programme of the future Atmospheric Sentinels, starting with S5P which is estimated be launched in early summer 2016.

The mobile DOAS measurements were performed in Bucharest (44.43°N, 26.10°E) and Turceni (44.67°N, 23.38°E), but only above Turceni were possible DOAS flights. We here present a comparison between mobile DOAS observations (car and aircraft) and the space measurements provided by the Ozone Monitoring Instrument (OMI, 13x24km2) and the Global Ozone Monitoring Experiment-2 instrument (GOME-2, 40x80km2). The ULM-DOAS system, beside Car-DOAS observations, provides a promising tool for satellite validation, especially for space observations by high resolution sensors such as the future TROPOMI instrument. A key added value of the ULM-DOAS, illustrated in this work, is the capacity to investigate the spatial variability of NO2 and SO2 inside the horizontal extent of satellite pixels, e.g. above exhaust plumes.

Here we present the STRatospheric Estimation Algorithm from Mainz (STREAM), which was delevoped as TROPOMI verification algorithm.
STREAM is a modified reference sector method, which estimates the stratosphere over "clean" regions,
as well as over clouded scenarios in which the tropospheric column is shielded.
The selection of "clean" pixels is realized gradually by assingning weighting factors to the individual ground pixels,
instead of applying binary flags.
Global stratospheric fields are then compiled by "weighted convolution".
In a second iteration, unphysical negative tropospheric residues are suppressed by adjusting the weights respectively.

STREAM was applied to synthetic column densities as well as real measurements from GOME, SCIAMACHY, GOME-2, and OMI, and generally works well.
The advantages and limitations of STREAM with respect to similar (modified reference region) and complimentary (assimilation) stratospheric estimation schemes as well as the respective uncertainties and their impact on tropospheric column densities are discussed.

The addition of clouded measurements in the stratospheric estimation reduces the biases often found for modified reference region approaches, caused by the necessary interpolation over extended continental regions. We discuss how far this approach can be applied for geostationary satellite instruments, like Sentinel 4, where a clean reference region is not available, and found promising first results.

AerGom is a retrieval algorithm designed to analyse transmittance measurements from the GOMOS instrument (Global Ozone Monitoring by Occultation of Stars) and retrieve local atmospheric aerosol extinction (UV-Vis) along with concentration of trace gases such as ozone, NO2 and NO3. AerGom differs from the official GOMOS product in its versatile approach to aerosol parameterization, its improved calculation of the Rayleigh component of the signal and its potential to include the full-covariance matrix in its retrieval scheme, thus taking into account the correlation between all retrieved species during the spectral inversion.

In this work, we study the change of the retrieval algorithm output to modifications made to the retrieval parameters such as the reference wavelengths for the spectral parameterization, the implementation of the full-covariance matrix in the inversion process, the inclusion of different trace gases, and the selection of different spectral ranges for the retrieval. 

GOMOS being a stellar occultation experiment based on the observations of hundreds of different stellar sources, the results from this sensitivity study will also be analysed in the context of these stars characteristics, i.e. star temperatures and magnitudes, affecting the signal-to-noise ratio in different parts of the measured transmittance spectrum.

Aerosol_CCI is an ESA project initiated in the framework of the Climate Change Initiative (CCI). It aims at the development of data records and time series based on archives from ESA sensors and third party missions. The project includes tropospheric activities based on different sensors such as ATRS-2, AATSR, IASI, POLDER, etc., as well as stratospheric activities. The latest aspects are based on measurements made by the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which flew onboard Envisat and provided global measurements based on stellar occultation from 2002 till 2012.

In this work, we present an overview of the stratospheric aspects of the Aerosol_CCI activities. The stratospheric aerosol product from Aerosol_CCI is derived from the results of the AerGom retrieval algorithm. The AerGom retrieval allows for the determination of the aerosol extinction coefficient vertical profiles in the UV-Visible range. The data are analyzed for quality and consistency and then combined together to produce time series of a variety of binned product.

The Aerosol_CCI stratospheric data product contains the aerosol extinction coefficient, AOD and ångström exponent, provided as gridded products covering the entire ENVISAT period. A list of new additional datasets is currently in preparation, including more radiative parameters such as the single scattering albedo and the asymmetry factor, microphysical parameters as surface area density and volume density and various particle size parameters as well: effective radius, equivalent lognormal size distribution.

Beyond the algorithm developments and improvements over the projects lifetime, a significant progress was made by providing the users with several gridded products of various spatial and temporal resolution which are better adapted to their needs, more particularly for climate modelling applications.

As with any CCI project, an independent validation effort was conducted and the results of comparisons with ground-based lidars, sondes as well as other satellite instruments will be shown. We will also present comparison results between the stratospheric Aerosol_CCI product and the CCM EMAC model simulations, and show how the aerosol extinction dataset is used in the evaluation of the volcanic aerosol budget during the decade (in combination with the SO2 dataset provided by MIPAS), as well as for model validation applications.

In this sense, we will demonstrate that the stratospheric activities contribute significantly to fulfil the needs of the Climate Modelling Community in terms of resolution, characterization and data quality.

This contribution focuses on the operational GOME-2 trace gas column products developed in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Composition and UV Radiation (O3M-SAF). We present an overview of the retrieval algorithms for ozone, NO2, SO2, formaldehyde (CH2O) and water vapour, and we show examples of various applications such as air quality and climate monitoring, using observations from the GOME-2 instruments on MetOp-A and MetOp-B.

The retrieval of total ozone columns from GOME-2 uses an optimized Differential Optical Absorption Spectroscopy (DOAS) algorithm, with air mass factor conversions calculated using the LIDORT model.
Total and tropospheric NO2 is retrieved with the DOAS method in the visible wavelength region around the 435 nm. SO2 emissions from volcanic and anthropogenic sources can be measured by GOME-2 using the UV wavelength region around 320 nm. For CH2O, an optimal DOAS fitting window around 335 nm has been determined for GOME-2. The GOME-2 trace gas column products have reached the operational O3M-SAF status, and are available to the users in near real time (within two hours after sensing by GOME-2).

The use of trace gas observations from the GOME-2 instruments on MetOp-A and MetOp-B for air quality and climate monitoring purposed will be illustrated, e.g. for South-East Asia and Europe. Furthermore, comparisons of the GOME-2 satellite observations with ground-based measurements will be shown. Finally, the use of GOME-2 trace-gas column data in the Copernicus atmospheric service project MACC-III will be presented.

Atmospheric Optical Depth (AOD) is a key parameter in correcting for atmospheric effects in satellite data. Currently, AOD is derived from ground measurements of atmospheric aerosol properties made using sun photometers. These consist of multispectral band filter radiometers that measure the sun and sky radiances at a set of wavelengths within the visible and near infrared spectrum. By comparison with the exoatmospheric solar radiance a measure of the scattering properties of the atmosphere at the site of measurement is determined. Using spectral information, an estimate of aerosol quantities, water vapour and particle sizes and shapes can also be determined.

Currently, the traceability for sun photometers and in particular the AERONET (AErosol RObotic NETwork) is to master instruments at Mauna Loa Observatory in Hawaii maintained by NASA Goddard Space Flight Center. Field instruments are returned to Mauna Loa every six to twelve months for recalibration. The reference instruments are calibrated for direct sun measurements using the clean air assumed at the top of the mountain at Mauna Lao. For the sky radiance measurements, an integrating sphere calibration is performed using the NASA Goddard calibration facility, with an uncertainty of 5%. A mirror site to Mauna Loa has now also been established in Lanzarote.

The ESA Ground-Based Air-Quality Spectrometer Validation Network (Pandonia) project is funding the development of a new generation of sun photometer based on a spectrometer developed by LuftBlick. The Pandora instrument is a spectrometer system for direct sun, sky radiance and direct moon measurements that can be used to calculate ozone and nitrogen dioxide column, and AOD over the range 300 nm to 900 nm.

As part of the EMRP project Metrology for Earth Observation and Climate (MetEOC2) we will determine methods to provide SI-traceability to AOD measuring instruments with a focus on the new ESA Pandonia networks instrumentation but also applicable to those used in the AERONET network. We will establish a means to transfer a laboratory-based method to any type of surface field AOD measurement instrument, facilitating innovation and a long term sustainable traceability method. Within this framework NPL is characterising the Pandora spectrometer for both solar disk and sky brightness geometries using the Spectrally Tuneable Absolute Irradiance and Radiance Source (STAIRS). This will provide a fully SI traceable radiometric calibration and an uncertainty analysis for the ESA network.

The EarthCare mission (Jaxa/ESA) aims at understanding the role of clouds and aerosols in the earth’s climate. Key components of the observation are the active sensing of atmospheric hydrometeors from Doppler Cloud Profiling radar (CPR) and high spectral resolution lidar (ATLID). This study reports on recent activities related to the development of the EarthCARE level 2 retrieval algorithms using these sensors for the global analysis of liquid phase hydrometeors and their improved representation in climate models. One of the expected improvements from current observations related to low level clouds is the better co-detection of low level clouds by both sensors and quantification of drizzle amount with Doppler information [Illingworth, A., and the EarthCARE team, BAMS, 2015]. Toward better quantification of low level cloud microphysics and radiative properties with the EarthCARE data, a physical model that practically simulates the multiply scattered depolarized lidar returns had been developed and incorporated into the ATLID retrieval algorithms. Taking Monte Carlo calculations as the reference, comparisons have shown that the space-time diagrams of the lidar returns arriving to the receiver from different vertical grids with varying cloud microphysics could be reproduced effectively by the approach. Using such approach in combination with the CPR, information content of low-level cloud physical properties such as cloud base, amount of liquid phase within mixed-phase clouds, vertical distribution of particle size and number concentration of clouds and drizzle are studied, which are still complicated to understand from satellites. Further, similar sets of existing satellite and ground-base active remote sensing measurements, e.g., the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar, CloudSat radar, and the Multi-field of view-Multiple scattering-polarization lidar (MFMSPL) system, are used to demonstrate the performance of the EarthCARE cloud products. The MFMSPL system have been operated in Japan, and is capable of measuring optically thick part of liquid-, mixed-phase clouds based on the multiple-scattering lidar concept, and are used for the evaluation of the EarthCARE level 2 products.

We describe the network of ground-based ultraviolet (UV) radiation monitoring stations in Belgium. The observed cumulative irradiances -UVB, UVA and total solar irradiance (TSI)- over the course of measurement for three stations -a northern (Ostende), central (Uccle) and a southern (Redu)- are shown. The longest series of measurement shown in this study is at Uccle, Brussels, from 1995 till 2014. Thus, the variation of the UV index, together with the variation of irradiances during summer and winter months at Uccle are shown as a part of this climatological study. The trend of UVB irradiance over the above mentioned three stations is shown. This UVB trend is studied in conjunction with the long-term satellite-based total column ozone value over Belgium, which shows two distinct trends marked by a change point. The total column ozone trend following the change point is positive. It is also seen that the UVB trend is positive for the urban/sub-urban sites: Uccle and Redu. Whereas the UVB trend at Ostende, which is a coastal site, is not positive. A possible explanation of this relation between total column ozone and UVB trend could be associated with aerosols, which is shown in this paper by means of a radiative transfer model based study -as a part of a preliminary investigation. It is seen that the UVI is influenced by the type of aerosols. 

This study also lays the background for future detail studies related to the ground-based UV network data together with satellite-based studies. The interaction between irradiance-clouds-aerosols will be exploited.

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) measured the atmospheric limb emission in the thermal-infrared spectral region on board the ENVISAT satellite in the time span from June 2002 to April 2012. We used MIPAS data to study seasonal and latitudinal variations of propane (C₃H₈) and ethane (C₂H₆) in the upper troposphere lower stratosphere (UTLS) region. Retrievals were made using the 700-800 cm^-1 spectral region, where new spectroscopic data of ethane had become available. To make feasible the joint retrieval of propane and ethane from their extremely weak spectral features, we used the Optimized Retrieval Model (ORM), the scientific prototype of the ESA level 2 processor for MIPAS, upgraded with the Multi-Target Retrieval (MTR) functionality and with the possibility to use Optimal Estimation (OE) to apply external constraints to the state vector. In order to evaluate the sensitivity of MIPAS measurements to seasonal and latitudinal distribution of C₃H₈ and C₂H₆ we analysed a dataset containing measurements in nominal observation mode acquired in selected dates of each month of the year 2008. Preliminary results on propane and ethane global UTLS distribution and on their seasonal behaviour will be presented.

Plants emit a variety of VOCs affecting the chemical and physical properties of the atmosphere. This work presents observed changes in ethane (C₂H₆), acetylene (C₂H₂) and Peroxyacetyl nitrate (PAN). Ethane is the most abundant hydrocarbon in the Earth’s atmosphere, after methane, and acts as a precursor to tropospheric ozone. Sources of ethane include fossil fuels, the flaring of natural gas, biomass burning and biofuel use, with minor oceanic and biogenic sources. It’s relatively long lifetime of 2 months is sufficient to allow mixing of the compound throughout the troposphere and lowermost stratosphere (when conditions allow). Acetylene is produced from combustion and burning processes with the dominant source from biofuels and plays an important role in the formation of glyoxal (CHOCHO) with implications for the production of secondary organic aerosol (SOA). The lifetime of C₂H₂ is of the order two weeks. PAN is an important odd-nitrogen compound (NOy) in the atmosphere as it acts as a reservoir of nitrogen oxides (NOx =NO+NO₂). The long lifetime of PAN at the cold temperatures of the upper troposphere (of the order several weeks can allow NOx to be sequestered and transported until it is released at lower and warmer altitudes, potentially resulting in ozone production in remote regions.

Recent studies (e.g. Simpson et al., Nature, 2012) have highlighted a long-term decline of global atmospheric ethane concentrations and discuss implications for methane abundance. The most likely cause of the decrease was inferred to be from decreased venting and flaring from oil fields. Other sources were likely to have remained fixed. Their work focussed on surface sites across the Pacific Ocean. In this study, investigate the changes in ethane occurring in the upper troposphere and lower stratosphere from a global perspective utilise MIPAS data between 2002 and 2012. Little work has been done on the long-term changes of C₂H₂ and PAN in the upper troposphere.

Data from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard Envisat have high vertical and spectral resolution (1.5 km intervals between 6 km and 21 km in optimised resolution mode with a spectral sampling of 0.0625 cm^-1). The high spectral resolution with good signal to noise allows differentiation of up to 30 species in MIPAS spectra. We use the v7 of the MIPAS l1b dataset for the analysis, where the impact of the ageing of the detectors on non-linearities has been corrected, which is important for trend estimation.

By using a simple linear approximation, taking into account variability to define errors on the fit, we derive largest decreases of ethane in the upper troposphere (300K-340K), with the Northern hemisphere changes significantly larger than the Southern Hemisphere. Although smaller, the southern hemisphere decreases are significant (up to 5 ppt/yr). As there is likely to be a minimal contribution from venting, this is most likely linked to changes in biomass burning over that period. It is shown that there exists a relationship between MIPAS ethane and methane growth rate in the upper troposphere, with peaks in ethane concentration linked to methane growth rate increases. Changes in C₂H₂ and PAN are less marked and appear to be driven mainly by seasonal cycles and variability in temperature (for PAN). The stability of new MIPAS v7 data is confirmed by analysis of N₂O which shows a similar modest increase compared to surface sites.

The Fourier transform spectrometer (FTS) is an important instrument for remote sensing spectral applications. In the recent past a new class of FTS has been developed, the Imaging Fourier transform spectrometers (IFTS). An IFTS uses a two dimensional detector array like a camera instead of a single detector. This enables a high spacial sampling combined with a good spectral resolution. The Gimbaled Limb Observer for Radiance Imaging of the Atmosphere (GLORIA) is an airborne IFTS and designed to improve the knowledge of the upper troposphere / lower stratosphere region. The typical array size of GLORIA is 128 × 48. The spectral coverage is 780 − 1400cm−1 with a maximal optical path difference of 8cm.

The new technique of IFTS brings new challenges and advantages for the data processing. The raw data are interferograms (IFGs) sampled on a time equidistant grid which need to be processed such that every IFG is sampled on the same equidistant spacial grid. This is the main algorithm of so-called level 0 processing. The large data of GLORIA measurements leads to huge I/O and memory footprint for an in memory level 0 processing (around 17GB allocated memory for a single measurement). In order to reduce the memory consumption a new pipeline level 0 processor is presented. Instead of loading the whole data into the memory, the data is streamed and processed online by processing nodes. Since only the memory in the node is allocated the resource consumption is static. The new architecture comes with some challenges. Some of the original level 0 algorithms require global information over IFG like the position of the zero optical path difference or the length of the data. This information is now estimated online and updated continously during the process. For the resampling, a ring buffer is used which store a part of the data. The ideal size of the ring buffer, keeping a good balance between performance and error level, has been determined through a series of performance studies. The studies show that for a moderate buffer length (around 40 sampling points) the error is below the noise level. Every original algorithm could be replaced by a direct alternative or parameter are estimated at runtime and stores for further processing.

The new architecture can also be applied during the measurement. The seperation in computation nodes enable a trivial parallelization and a good maintainability. The pipeline processor reduces the usage of memory to a constant of only a few MBs for the whole level 0. 

Absorbing aerosols greatly influence climatological cycle by increasing global warming caused due to greenhouse gasses. The drastic increase in the concentration of absorbing aerosols suspended in the atmosphere may be attributed to anthropogenic activities like biomass fuel burning, urban and industrial emissions, emission from diesel engine etc. These sources majorly emit black carbon and dust which may warm the climate regionally or globally. Over the past decade, Asian countries have experienced an abrupt rise in the concentration of absorbing aerosols in the atmosphere. A very conspicuous rise has been observed in South Asia constituting Arabian Peninsula and Indian sub-continent where the major sources of absorbing aerosols are desert and soil dust, fossil fuel and biomass burning, these emissions are subject to seasons. On the other hand, in fast-growing countries like Russia, China, Japan, Korea, India, air pollution caused due to industrial and vehicular emissions have been found to remain persistent annually and increase over decades. In this study, AAOD at 500 nm wavelength obtained from Aura OMI Aerosol Global Gridded Data Product-OMAEROe (V003) retrieved using Multi-wavelength algorithm at a spatial resolution of 0.25°×0.25° has been used to analyse the annual trend of absorbing aerosols over 10 years i.e. from 2005 to 2014 over Asian region. The trends have been studied using Linear Regression Model significant at a Confidence Level > 95% (p-value <0.005).
Keywords: Aerosol, Annual trend, Asia, Aerosol Absorption Optical Depth, Aura, OMI, OMAEROe(V003), Black Carbon
Abbreviations: OMI: Ozone Monitoring Instrument

Satellite Remote sensing data represent a unique opportunity to monitor the volcanic emission on large areas with adequate spatial and spectral resolution. In the late 1980s it was shown that volcanic ash clouds can be discriminated from meteorological clouds on remote sensing images by considering two specific thermal infrared channels of multispectral satellite-borne sensors. Ash particles of radius ranging between 1 μm to 10 µm absorb more infrared radiation around 11 µm, while water droplets or ice particles absorb more  around 12 μm. Thus, the brightness temperature difference (BTD) of a multispectral image between channels centred at 11 μm and 12 μm will be negative for ash clouds, and positive for meteorological clouds. On this finding, several works, aimed to characterise eruption phenomena by a number of satellite missions have been based. Such studies have proven the key role of remote sensing data as a primary information source to detect volcanic ash clouds and retrieve ash particles properties like effective radius, optical thickness and the total column mass in the ash plume. Moreover, some sensitivity analysis studies pointed out the importance of the ancillary information to constrain the output of radiative transfer models and how the accuracy of inversion methods is affected by errors even equal to 40-50%. In the thermal infrared range also SO2 content of volcanic clouds can be estimated from multrispectral satellite imagery by considering the satellite channels centred at 8.7 μm.

The application of Neural Networks (NNs) to detect ash and SO2 has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the BTD. Moreover, the overlapping of ash plume and meteorological clouds causes a mixed behaviour making difficult to identify the “ashy pixels” and even more difficult the retrieval of ash and SO2 parameters.

These problems have been carefully addressed by the NNs proposed methodology. A fully automatic procedure, aimed to characterise the eruption images, identifying some main classes such as meteorological clouds and ash clouds, has been presented in our previous publications. In this approach, the ashy pixels are divided considering the surfaces underlying the ash cloud (e.g. ash on sea, ash on meteorological clouds etc.). Moreover, the principal surfaces like sea, ice, land and sand are identified on the satellite image. The information about the surface under the ash cloud can improve the application of the retrieval procedure since it requires the surface temperature estimation. The well-known methods used for the simultaneous quantitative estimation of ash and SO2, based on comparisons between top-of-the-atmosphere (TOA) radiance and the simulated radiance obtained using a radiative transfer model (RTM), require a high computational time and many parameters as input, making the near real-time application of the standard retrieval procedures difficult during volcanic crises. For these reason, the NNs capability to behave as a universal approximator have been exploited to retrieve the ash parameters and the SO2 abundance. The training of the NNs algorithms has been performed by means of RTM simulations. In our previous works we showed that the NNs approach achieves an accuracy comparable to the consolidated approach based on look up tables, with the advantage of the speed up due to the parallelism of the algorithm. In some cases, NNs are expected to outperform other approaches thanks to their non-linear approximation capability. In this study, a complete framework to fully characterize a volcanic eruption is provided considering MODIS and MSG SEVIRI data acquired on eruptions from different volcanoes.

The TROPOMI (Tropospheric Monitoring Instrument) is the payload instrument for the Sentinel 5 Precursor (S5P) mission which will provide atmospheric composition products including ozone during the time frame from 2016 to 2022. It will consequently extend the data record initiated with GOME/ERS-2 and continued with the SCIAMACHY/ENVISAT, OMI/AURA and GOME-2/MetOp missions.

Here we present the Near-Real-Time (NRT) TROPOMI/S5P total ozone retrieval algorithm which is based on the "DOAS-style" GOME Data Processor (GDP) algorithm Version 4.x. The DOAS technique for total ozone retrieval was deployed from the start of the GOME/ERS-2 mission in 1995 and is currently being used for the generation of the ESA SCIAMACHY and EUMETSAT O3M-SAF GOME-2 operational products. The enhancements in GDP 4.8 (the latest version of the GDP 4.x algorithm) are described first, and then we present the Global validation results for GOME-2/MetOp-A (GOME-2A) and GOME-2/MetOp-B (GOME-2B) total ozone measurements using Brewer and Dobson measurements as references.

One of the major challenges for the operational processing of TROPOMI/S5P measurements is the high data rate - two orders of magnitude more data than that from GOME-2. Here we discuss performance enhancements of the retrieval algorithms such as the development of an acceleration method for Radiative Transfer Model (RTM) simulations. The improvement of total ozone retrieval algorithm special for TROPOMI including DOAS and AMF calculation  will also be presented.

The Airborne ROmanian Measurements of Aerosols and Trace gases (AROMAT) campaigns and its follow-up AROMAT-2 were held respectively in September 2014 and August 2015. Both campaigns focused on two geophysical targets: the city of Bucharest and the large power plants of the Jiu Valley, which are located in a rural area 170 km West of Bucharest. These two areas are complementary in terms of emitted chemical species and their spatial distributions.

The objectives of the AROMAT campaigns were (i) to test recently developed airborne observation systems dedicated to air quality satellite validation studies  such as the AirMAP imaging DOAS system (University of Bremen), the NO₂ sonde (KNMI), and the compact SWING whiskbroom imager (BIRA), and (ii) to prepare the validation programme of the future Atmospheric Sentinels, starting with Sentinel-5 Precursor (S5P) to be launched in early summer 2016.

We present results from the different airborne instrumentations and from coincident  ground-based measurements (lidar, in-situ, and mobile DOAS systems) performed during both campaigns. The AROMAT dataset adresses several of the mandatory products of TROPOMI/S5p, in particular NO₂ and SO₂ (horizontal distribution and profile from aircraft, plume image with ground-based SO₂ and NO₂ cameras, transects with mobile DOAS, in-situ), H₂CO (mobile MAX-DOAS), and aerosols (lidar, sun-photometer and aircraft in-situ). The experience gained during AROMAT and AROMAT-2 will be used in support of a large-scale TROPOMI/S5p validation campaign in Romania in summer 2017.

The scientific and industrial communities are being confronted with a strong increase of satellite missions and related data. This is in particular the case for the Atmospheric Sciences communities, with the upcoming Copernicus Sentinel-5 Precursor, Sentinel-4, -5 and -3, and ESA’s Earth Explorers scientific satellites ADM-Aeolus and EarthCARE.

The challenge is not only to manage the large volume of data generated by each mission / sensor, but to process and analyze the data streams. Creating synergies among the different datasets will be key to exploit the full potential of the available information. Integrating Earth-Observation data with ground based observations and numerical models, is the basis for a new data exploitation paradigm which opens new research and commercial opportunities.

As a preparation activity supporting scientific data exploitation for Earth Explorer and Sentinel atmospheric missions, ESA is funding the technology study and prototype implementation of the “Technology and Atmospheric Mission Platform” (TAMP). Services and tools are developed along use cases defined with users from different scientific and operational fields and implemented according to their requirements to ensure acceptance of TAMP platform by the atmospheric community.

The TAMP test-bed environment offers data access, visualization, processing and analysis services for the “data triangle” consisting of (1) EO satellite products, (2) model data provided by chemical weather forecast, and (3) reference / validation data sets. At this stage the system shall host more than 20 global collections of EO-based atmospheric products (e.g. Ozone, Aerosol, NO2, …) collected by 10 different satellite sensors, the simulations from five different models (e.g. hourly 3D distribution of ozone, NO₂, PM10,  and other pollutants at European and regional scales) and 17 collections of validation / correlative data (e.g. AERONET, EARLINET, NDAAC or Pandonia), for an overall data volume of more than 3TB.

The implementation pursues the “virtual workspace” concept: all resources (data, processing and collaboration tools) are provided as “remote services”, accessible through a standard web browser, to avoid the download of big data volumes and for allowing utilization of provided infrastructure for computation.

The current work aims at presenting the TAMP platform (concepts, implementation) and the preliminary results obtained by the involved Atmospheric Science community.

Recent progress in the satellite imaging technology at visible and near IR spectral range has enabled marked advances in capturing of various nocturnal land/sea and atmosphere phenomena from the low Earth orbit platform; atmospheric nightglow being among these. On dark moonless nights, the Suomi-NPP satellite and its very sensitive Day/Night Band (DNB) low-light visible sensor is able to discern very faint nightglow emission structures when present in the scene.  The unique observations reveal a complex and dynamic environment replete with waves in the form of airglow brightness fluctuations.  These structures are a manifestation of complex interactions between propagating atmospheric gravity waves and the temperature/density structure of a geometrically thin atmospheric layer present between ~ 85-95 km (near the mesopause). The responsible gravity waves can be generated by several meteorological and geophysical mechanisms; in this presentation we will show several examples of such DNB-observed gravity waves in nightglow, whose properties vary according by forcing mechanism.  We will show how it is possible to track the source of these waves using the satellite imagery, and also how to distinguish unambiguously the mesospheric nightglow signals from the underlying tropospheric clouds and other terrestrial sources of nocturnal light emission.

While the atmospheric gravity waves themselves can be detected by several other observational methods, the significance of the DNB observations of wave structures in the nightglow is in their unprecedentedly high spatial resolution (0.75 km; over an order of magnitude finer than the current state of the art nadir-viewing sensors dedicated to these measurements), revealing very fine detail structures, as well as their large horizontal scale coverage. In light of the novel information content and the crude state of upper atmospheric wave parameterization in climate models, these observations hold potential to significantly advance our current understanding of the land, sea, and lower/upper atmospheric interactions and energy transfer.

The DNB will be flown also on the future NOAA/NASA Joint Polar Satellite System (JPSS) satellites.  The authors are not aware of any considerations for an instrument of similar caliber, such as the Low Light Imager once proposed for the EPS-SG constellation or Sentinel missions.  The ultimate goal of including a low-light visible capability upon the operational geostationary platform remains on the distant horizon, but continued discoveries of scientific and operational utility help to build a compelling case for demonstration of a research-grade system in the near-term.

Long-term observations of clouds are essential for climate monitoring and climate model evaluation. Cloud property data sets derived from passive sensors onboard the polar orbiting satellites have global coverage and now span a climatological time period. The cloud properties dataset covering the 1982-2012 period derived from Advanced Very High Resolution Radiometer (AVHRR) has been recently produced by the European Space Agency project for studies of cloud properties in the Climate Change Initiative programme (ESA-Cloud-CCI). The 31y cloud fraction dataset builds on eight consecutive satellite missions (from NOAA 7 to NOAA 19), which could potentially be a source of an inhomogeneity in the cloud fraction time series.

This study presents an evaluation and a homogeneity analysis of a mean monthly cloud fraction over Europe. These are performed based on a comparison against synoptic cloud cover observations at hundreds of stations over Europe. Results show overall good agreement between satellite-derived and synoptic cloud fraction (mean bias = 4%; root mean square error = 10%), however a few suspicious monthly means has been spotted for NOAA-11, 12 and 14. Temporal stability has been first assessed by means of absolute Standard Normal Homogeneity Test (SNHT) that reveals there is no significant inhomogeneity in the ESA-Cloud-CCI cloud fraction dataset. Second, the difference between standardized monthly anomalies of ESA-CCI and SYNOP has been computed, which shows a significant negative trend exceeding 1% per decade. The relative SNHT performed on the computed anomaly difference reveals a potential inhomogeneity in 2001 (between NOAA-14 and NOAA-16).  Notwithstanding the investigated stability issues,  the ESA-Cloud-CCI cloud fraction is potentially suitable for the study of the means and trends over Europe. The preliminary results show that statistically significant negative trend in cloud fraction of approximately -0.5 % per decade can be observed over water and polar tundra climatic zone.

Air pollution is one of the most important environmental problems in developing Asian countries like China. In this region, studies showed that the East Asian monsoon plays a significant role in characterizing the temporal variation and spatial patterns of air pollution, since monsoon is a major atmospheric system affecting air mass transport, convection, and precipitation.

Knowledge gaps still exist in the understanding of Asian monsoon impact on the air quality in China under the background of global climate change. For the first time satellite observations of tropospheric ozone and its precursors will be integrated with the ground-based, aircraft measurements of air pollutants and model simulations to study the impact of the East Asian monsoon on air quality in China.

We apply multi-platform satellite observations by the GOME-2, IASI, and MOPITT instruments to analyze tropospheric ozone and CO, precursors of ozone (NO2, HCHO and CHOCHO)and other related trace gases over China. Two years measurements of air pollutants including NO₂, HONO, SO₂, HCHO and CHOCHO at a regional back-ground site in the western part of the Yangtze River Delta (YRD) in eastern China will be presented.The potential of using the current generation of satellite instruments,ground-based instruments and aircraft to monitor air quality changes caused by the East Asian monsoon circulation will be presented. Preliminary comparison results between satellite measurement and limited but valuable ground-based and aircraft measurements will also be showed.

In the framework of the ESA Aerosol_cci project, three 17-year 1995 - 2012 ATSR-2 (ERS-2) and AATSR (ENVISAT) datasets have been made available to users (data v4.21 from Uni. of Swansea, ADV v2.3 from FMI, and ORAC v3.02). In addition to AOD, Ångström exponent, and other products, all retrievals include pixel-level uncertainties.

Knowledge of uncertainty is essential to understand an observation. Uncertainty estimates are crucial for data assimilation and data merging (e.g. uncertainty-weighted ensembles). Therefore, the estimation of the uncertainties in these data series is an important task to ensure their best possible usage. Although essential, thus far an evaluation of pixel-level uncertainties is uncommon in aerosol products.

Here, we evaluate the uncertainty characterisation within the 17-year ATSR-2/AATSR data series. An initial validation of the uncertainties on AOD are performed under the assumption that, for all algorithms, the uncertainty should be dominated by the retrievals themselves. They are expected to be much larger than the uncertainties in AERONET (direct) AOD observations, providing a straightforward means to check the consistency of the retrievals. Co-located AERONET observations are subtracted from retrieved values. When divided by the retrieved uncertainty, the results should form a Gaussian distribution with mean zero and a width of unity. Significant deviations from that would indicate systematic errors not accounted for in the existing error propagation. We present our validation results, where we have put special emphasis on the evaluation of the temporal consistency within the three 17-year time-series and potential regional patterns.

Furthermore, we show the evaluation of a 4-month ATSR-2/AATSR test data set (for March, June, September, December 2008) to better understand the translation of pixel-level uncertainties to Level 3 (daily and monthly data, 1°x 1° lat - lon grid). The following products are calculated to identify their suitability as an approximation for the uncertainty in Level 3 data: mean of pixel-level uncertainty; the standard deviation for the pixels; the propagation of pixel uncertainties into the average value; the sum of the last two; and a worst­-case propagation, being the difference between the means of (AOD + standard deviation) and (AOD standard deviation).

We have used global scale Thermal infrared measurements of mid-to-upper tropospheric nitrous oxide (N₂O) from the Greenhouse gases Observing SATellite (GOSAT) and outputs from the 3D Chemical Transport Model (CTM) LMDz-OR-INCA to assess the impact of the Indian subcontinent N₂O emissions on the N₂O field in the eastern Mediterranean basin (MB) in summer. The use of nitrogen fertilizer coupled with high soil humidity during summer monsoon period produced high emissions of N₂O in many south Asian countries and especially the Indian subcontinent. N₂O is transported  to the upper troposphere by updrafts associated to the monsoon and redistributed westward to the eastern MB. This summertime (June-July-August) enrichment in N₂O in the eastern MB produces a maximum in the east-west difference of MB mid-to-upper tropospheric N₂O anomaly representative for the period 2010-2013 with a peak in July observed by GOSAT (~1.8 ± 0.3 ppb) consistently with LMDz-OR-INCA (~0.5 ppb). This summertime enrichment of N₂O over the eastern  MB is consistent with the increase of  the surface emissions and the convective precipitations over the Indian subcontinent during the summer monsoon period. N₂O over the eastern MB can therefore be considered as a footprint of Indian summertime emissions.

Here, we present first results from a new ESA project SAMIRA, which aims at improving regional and local air quality monitoring through synergetic use of data from present and upcoming satellites, traditionally used in situ air quality monitoring networks and output from chemical transport models. Through collaborative efforts in four countries, Romania, Poland, The Czech Republic and Norway, all with existing air quality problems, SAMIRA intends to support the involved institutions and associated users in their national monitoring and reporting mandates as well as to generate novel research in this area.  

Despite considerable improvements in the past decades, Europe is still far from achieving levels of air quality that do not pose unacceptable hazards to humans and the environment. Main concerns in Europe are exceedances of particulate matter (PM) and ground-level ozone, benzo(a)pyrene (BaP)  and nitrogen dioxide (NO₂). While sulfur dioxide (SO₂) emissions have fallen, regional emissions can still be high.

 

The objectives of SAMIRA are to improve algorithms for the retrieval of hourly AOD maps from SEVIRI, and to retrieve robust methods to derive the column and near-surface PM maps for the study area by combining satellite AOD with regional modelling.  The benefit to existing monitoring networks (in situ, models, satellite) by combining them using data fusion methods will be tested for satellite based NO₂, SO₂, and PM/AOD. Furthermore, SAMIRA will test and apply techniques for downscaling air quality-related EO products to a spatial resolution that is more in line with what is generally required for studying urban and regional scale air quality. This will be demonstrated for a set of study sites that include the capitals of the four countries and the highly polluted areas along the border of Poland and the Czech Republic, and the Gorj County in Romania. All data products shall undergo a quality control, i.e. robust and independent validation.  Potentially, the SAMIRA consortium will be extended to up a pre-operational system for improved PM₁₀ forecast using observational (in situ and satellite) data assimilation (optional proposal). SAMIRA aims to maximize the project benefits by liaison with national and regional environmental protection agencies and health institutions, as well as related ESA and European initiatives, and the upcoming operational and research orientated Copernicus Atmospheric Monitoring Services (CAMS).

 

Ozone is a greenhouse gas and one of the most important species affecting air quality owing to its impact on human health and ecosystems. Ozone in the atmosphere is highly variable depending on the location and altitude. Because of its diurnal variability and likely sudden peaks, it is necessary to make observations of ozone at high temporal and spatial resolution. One of the challenges concerning air quality is to monitor ozone from space to add value to the existing global observing system comprised of the ground based station network and satellite measurements.

We present results from state-of-the-art Observing Simulated System Experiments (OSSEs) performed to define the optimal satellite instrument for ozone measurements. Using these OSSEs, we compare the MAGEAQ (Monitoring the Atmosphere from Geostationary orbit for European Air Quality) geostationary satellite concept to future scheduled satellite missions such as Sentinel-4. We discuss and quantify the added value of MAGEAQ to monitor ozone in the boundary layer.

Tropospheric ozone is the third most important anthropogenic greenhouse gas and a significant surface pollutant.  Despite decades of research, there are still gaps in our understanding of tropospheric O₃.  We do not completely understand of the mechanisms by which anthropogenic emissions of NO_x radicals are transported to the remote troposphere to impact local O₃ production. 

We do know that peroxyacetyl nitrate (PAN) plays a fundamental role in the long-range transport of O₃ and the global O₃ budget. Formation of PAN provides a thermally unstable reservoir for NO_x radicals, enabling long-range transport at cold temperatures and eventual release in the remote troposphere where these radicals are most efficient at producing O_3.  Without PAN chemistry the distribution of O₃ would be very different, with higher values in NO_x source regions and much lower values in remote regions of the troposphere. However, the relationship between PAN and O₃ is complex, and it tends to be poorly represented in global chemical transport models. PAN is a particularly difficult compound to capture and validate in models because many factors impact the production and lifetime of this species.  Until recently, the suite of available PAN measurements has been insufficient for rigorous testing of PAN representation in models.

Recent observations of PAN from space have the potential to drastically improve our understanding of tropospheric composition.  Here, we present new observations of PAN in the troposphere from the Tropospheric Emission Spectrometer (TES), flying on the NASA Aura satellite since 2004, and from the Cross-track Infrared Sounder (CrIS) on the Suomi-NPP satellite, flying since 2011.  We present multi-year PAN observations over selected regions (Asia, the United States and the tropics) and discuss the implications of the observed PAN concentrations for the role of fires, PAN precursor emissions and dynamics on the global distribution of PAN and the intercontinental transport of tropospheric ozone.

H2O plays a very important role in the upper troposphere and stratosphere. It has a strong radiative effect and it plays a key role in the ozone chemistry, being a source of HOx species involved in the catalytic destruction of ozone. The evolution of H2O in the lower stratosphere during the last decades is still not well determined. Contradictory results are obtained depending of the source of data (balloons, satellites).

H2O measurements by GOMOS (Global Ozone Monitoring by Occultation of Stars) on board Envisat can play a significant role in this area. The two advantages of the stellar occultations method are the self-calibration nature and the well-defined geometry. The IPF 6 algorithm provides greatly improved H2O profiles compared to IPF5 due to a correction of the intra-pixel PRNU. However there is still some room for improvement. A new algorithm has been developed in which the wavelength assignment is improved, the new HITRAN 2012 database is used for H2O absorption and a Levenberg-Marquartdt method is applied for spectrum fitting instead of using look-up tables for the estimation of H2O slant columns. This new algorithm provides improved H2O profiles to be used for studies on H2O variability and trends in the UTLS.

These studies have been performed in the framework of ESA-funded ALGOM (GOMOS Level 2 algorithm evolution studies) project. 

Monitoring surface-level air quality is often limited by in-situ instrument placement and issues arising from harmonisation over long timescales. Satellite instruments can offer a synoptic view of regional pollution sources, but in many cases only a total or tropospheric column can be measured. The measured column therefore requires partitioning to separate the free tropospheric and stratospheric influences from the boundary layer contribution. Typically this partitioning is performed using a chemical transport model, which can introduce additional biases arising from model accuracy and the often coarser grid resolution compared with the nadir satellite footprint.

This work describes an empirical technique to estimate daily surface-level NO₂ through combining tropospheric columns measured by OMI and surface in-situ measurements. Tropospheric NO₂ columns retrieved by OMI over China and the UK are combined with in-situ measurements to derive surface-level NO₂ pollution maps. Conversion to surface concentrations achieved through ratioing columns with in-situ measurements taken within the nadir footprint during the satellite overpass. These ratios are then applied to adjacent local satellite ground pixels, allowing for daily high-resolution (13 x 24 km²) mapping of surface NO₂.

Validation of these maps is achieved through comparisons with in-situ measurements and model data. Surface concentrations derived with this technique show better correlation than direct comparisons with the raw column data.

Cloud information from UV/VIS/NIR spectrometers aboard spacecraft platforms is highly valuable for the construction of cloud property climatologies, since it is complementary to the information retrieved using IR spectrometers, imagers and active sensors (cloud radars and lidars).

Clouds, when present, are the main modulators of the radiation transport across the atmosphere. They are very diverse in terms of composition (ice, liquid water), thickness (thin cirri to thick cumulonimbi), variability (homogeneous stratocumuli to inhomogeneous cumuli), altitude, etc. Consequently, the modeling of the cloud-radiation insteraction is one of the most challenging tasks in the atmospheric science.

The distance that photons travel across the atmosphere before being captured by a spaceborne instrument can be greatly affected by clouds. Absorption by atmospheric molecules is not only proportional to the molecular number density but also to the photon path legth.  Therefore, the effects of clouds on the photon path legth is of particular interest in the atmospheric remote sensing.  During the retrieval processing, changes in the absorption bands within the spectra are analyzed. These changes can be linked to variations in the molecular number density but also to variations in the photon path legth. Therefore, precise cloud information to correct for variations in the photon path length is  mandatory for the accurate retrieval of atmospheric trace gases.


The ROCINN algorithm retrieves cloud top height (pressure), cloud optical depth and cloud albedo  rom measurements in and around the oxygen A-band (~760nm) taking as input the cloud fraction computed with the OCRA algorithm (a color space approach based on broadband reflectances). This paper presents the latest version of the ROCINN algorithm as implemented to process Sentinel-5p, Sentinel-4 and Sentinel-5 data. There are two variants of the ROCINN algorithm: one that treats clouds as reflecting boundaries (CRB)) (i.e. Lambertian equivalent reflectors) and a second one that treats  louds as scattering layers (CAL). The ROCINN-CRB algorithm has been successfully used operationally for GOME/ERS-2 and GOME-2/MetOp-A and -B instruments since over one decade. The CAL algorithm treats clouds in a more realistic way and provides cloud properties closer to reality.  ROCINN V3.0 in the CRB and CAL variants have been included into the latest UPAS operational processor and is the baseline algorithm for the generation of cloud products from the atmospheric Sentinels.

In this work, we present and analyze preliminary ROCINN cloud properties from S5P/TROPOMI, in addition to those from GOME-2 on both MetOp-A and Metop-B. A quantitative comparison with independent cloud products, e.g. from VIIRS on NASA/Suomi-NPP, from AVHRR on MetOp and MODIS on NASA-EOS/Terra is forseen.

Independent assessments of cloud detection and cloud fraction are notoriously difficult to achieve with existing methods. Most of these have relied either on visual observers or compared time series of vertical profiles of cloud fraction with instantaneous horizontal distributions from satellite. Over the last few years visible multispectral low resolution (161 x 121 pixels) sensors have been employed for comparing against cloud fractions determined from satellites. However, these are restricted to daylight hours, have partial coverage as the solar disk is obscured and are only able to compare cloudless, fully cloudy or partially cloud skies.

 

The Blue Sky Imaging Limited’s ASTIC (All Sky Thermal IR Camera) consists of a ferro-electric sensor (320 x 240 pixels) and a compound fish-eye lens which operated near continuously from 2006-2012. This enables day and night imaging irrespective of cloud cover or illumination conditions. ASTIC is located at the Chilbolton Facility for Radar Research for a long-term programme of performing every 30 second measurements on cloud cover and is in coincidence with an AERONET sensor, lidar sensors and mm-radar sensors. Utilising a simple threshold based method on the temperature contrast image, clouds can be differentiated from sky straightforwardly and robustly. CFARR data was processed as part of an EU-FP CLOUDNET programme into cloud layer boundary heights and the lowest cloud-base height was then used to calculate the equivalent Field of View (FoV) for the sky. Only single layer clouds were chosen in this initial study which were thin (<1km).

 

For the time of overlap, all dates with cloud masks/cloud fraction from MERIS, VEGETATION, NASA-MODIS and MISR were chosen. The European sensors were all processed as part of the GlobAlbedo project using an uniform cloud mask to ensure compatibility. These cloud masks were then compared for the corrected FoV based on the CBH. The results of this inter-comparison will be shown focusing on why there were a number of false positives and false negatives for the EO-derived cloud masks and what this implies about the use of cloud statistics from EO.

During 2014 and 2015 the TROPOMI instrument, (soon to be) launched on Sentinel-5P, was calibrated on ground. A special on-ground calibration measurement method was devised and performed to determine the straylight with a relative 1e-7 accuracy. Correcting for straylight requires special algorithms due to the spatial and spectral distribution and variability of the straylight. We will show the details of the measurements,  the behavior of the straylight, the implemented  correction algorithms and their validation.

Recent developments in satellite remote sensing techniques have shown that plant fluorescence emissions can be retrieved from measurements by current satellite spectrometers. Fluorescence in the red and near-infrared wavelength range is a way for plants to release part of the energy gained through absorption by chlorophyll of solar radiation at shorter wavelengths. Fluorescence emissions are part of the complex biochemical and biophysical reaction chain in the plant's photosystems that converts atmospheric CO2 into sugars. The fluorescence spectrum has a characteristic shape and the physiological relations between fluorescence emissions and plant functioning have been established in the lab and in field experiments. In general, fluorescence emission is an indicator of photosynthetic efficiency and therefore of a plant's gross primary productivity.  
We have set up a retrieval of solar-induced vegetation fluorescence for GOME-2 following the general approach developed by Joiner et al. (AMT, 2013). In this method, the fluorescence contribution to top-of-atmosphere reflectance spectra is determined through a principal component analysis of reflectance spectra over non-vegetated areas. Our baseline implementation, however, differs in a number of aspects from the latest retrieval setup employed by these authors. For example, our fit window covers atmospheric absorption bands and as a consequence we use a larger number of principal components. Also, the selection of reference spectra differs: our reference set consists of a year of observations over desert areas only. We have currently processed the GOME-2A mission from its start in January 2007 until June 2015. The first purpose of this study is to provide a science verification of the statistical retrieval methods for vegetation fluorescence proposed by Joiner et al. (AMT, 2013) and Guanter et al. (RSE, 2012). As such, the data sets are compared in detail and additional experiments with GOME-2 data are performed to further illustrate retrieval sensitivities and assess uncertainties.
To further improve our understanding of the relationship between satellite-retrieved vegetation fluorescence and primary production, the fluorescence time series, spanning more than eight years now, is compared against gross primary productivity derived from flux tower net ecosystem exchange measurements. In this study, we focus on Australia and use data from the OzFlux network to analyse correlations between satellite-derived vegetation activity and locally derived vegetation photosynthesis. In Australia, a biome generally covers vaster areas and therefore flux tower measurements allow for a better comparison against measurements from GOME-2 with its relatively coarse spatial resolution.
Satellite-retrieved vegetation fluorescence is a promising parameter that may help to further constrain uncertainties in gross primary production and improve carbon cycle modelling. Its dynamic nature makes it a valuable addition to the existing greenness indicators. With the selection of the FLEX mission as the Earth Explorer 8 and the Sentinel-5P mission scheduled for launch in mid 2016, fluorescence data will continue to become available in the coming years.

Thanks to its covering of most of the thermal infrared spectrum, the IASI instruments flying onboard the European platforms Metop-A since 2006 and Metop-B since 2012 delivers global distribution of several Essential Climate Variables. In particular, IASI gives access to the mid-tropospheric burden of 3 greenhouse gases (GHG): methane, carbon dioxide and nitrous oxide, and thus contributes to the ESA Climate Change Initiative-GHG.

Using the 2 IASI instruments onboard Metop-A and -B, the variability and evolution of mid-tropospheric column of these gases will be analyzed over more than 9 years of observation, and discussed in light of the recent evolution of surface emissions and climate conditions. The combined use of both IASI, which are flying on the same orbit but with nearly half an orbit out of phase, yields a complete coverage of the Earth in one day.

Through comparisons with regular aircraft measurements of the CONTRAIL, CARIBIC and HIPPO programs, as well as observations made at the surface, we will show that IASI captures the trend and interannual variation of greenhouse gases, with an excellent agreement with the rate of increase measured at the surface, giving confidence in the ability of IASI to follow their evolution over the 20 years of EPS/MetOp program.

The ability to infer the space-time distribution of GHG total column and surface emissions from IASI observation will be discussed through assimilation exercises conducted with the ECWMF Integrated Forecast System in the framework of the setting of the Copernicus Atmospheric Service.

In the context of calibration/validation activities for ESA atmospheric missions, a project of the WEGC prepares and provides long-term radio occultation (RO) reference data from a variety of RO missions (in total up to about 2500 profiles per day). Applications of the data include use in climate change and variability studies, in the monitoring of trends in data from other spaceborne instruments, in validation of atmospheric satellite data products and in the validation of retrieval algorithms, in atmospheric process studies, and in bridging temporally separated space missions. The project ensures the provision of correlative RO data suitable for in-depth examination of tropospheric and stratospheric fundamental state profiles, such as of temperature, pressure, and humidity as function of altitude. This undertaking is highly worthwhile since the unique combination of global coverage, high accuracy and vertical resolution, long-term stability, and virtual all-weather capability makes, in the free atmosphere, the validation with RO data preferable to other methods.

The presentation will briefly discuss the system setup for multi-mission validation by RO at the WEGC, and the quality and availability of the reprocessed RO datasets. We will then focus on the discussion of results of the multi-year validation of temperature and pressure profiles over the upper troposphere and lower stratosphere (UTLS) from different versions of Envisat MIPAS data against collocated RO data processed at WEGC. In addition, the Envisat MIPAS datasets are compared with Radiosonde data from Vaisala RS90/92 sondes from the “standard” global radiosonde network. Additionally, we include the first years of GRUAN data (using Vaisala RS92), available since 2009. We show how these results help to obtain quantitative estimates on the quality of the data (e.g., systematic error bounds) and on the utility of RO to serve as reference data for the targeted validation and climate monitoring applications.

A general overview is provided of the Horizon 2020 project AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) funded by the European Union in the frame of the Call Space, EO-2-2015: Stimulating wider research use of Copernicus Sentinel Data.  The overarching objective of AURORA is to simulate the provision of synergistic data products for the vertical profile of atmospheric ozone and to assess their quality with respect to the one expected for the operational products of the geostationary (GEO) mission Sentinel -4 and of the Low Earth Orbit (LEO) mission as Sentinel-5p and Sentinel-5.

The project addresses key scientific issues relevant for synergistic exploitation of data acquired in different spectral ranges by different instruments on board the atmospheric Sentinels. A novel approach, based on the assimilation of GEO and LEO fused products by application of an innovative algorithm to S-4 and S-5 synthetic data, is adopted to assess quality of the unique ozone vertical profile obtained in a context simulating the operational environment. First priority is then attributed to the lower atmosphere with calculation of tropospheric columns and UV surface radiation from the resulting ozone vertical distribution.

In parallel, AURORA tackles the technological challenges of creating the infrastructure, exploiting virtual machines and cloud data sharing, to implement the data processing chain, including a geo-database and web-services for data access. The infrastructure represents a best practice that plays a key role in ensuring wider use of Copernicus Sentinel data for academia, public agencies and industry. It is the basis for a market analysis for pre-market applications and uptake in commercial communities. Strategic dissemination and exploitation is targeted to European level (academia, CAMS, GEOSS) and international level (potential synergies and data exchange will be investigated with TEMPO and GEMS, in USA and ASIA).

This presentation offers a first introduction to the three years project AURORA (February 1^st, 2016 – January 31^st, 2019) and describes the scientific, technological and application-oriented concepts acting as the pillars of the proposed research and development activities. The scope and objectives of the project, the work plan, the expected outcome, as well as the long-term perspectives, are illustrated in some details. In conclusion, the progress status, along with a summary of the results obtained in the preliminary and first phase of the program, is reported. Furthermore, it will be possible to follow the subsequent steps and the dissemination initiatives of AURORA by using the list of references and contact points indicated at the end of the overview.

Aerosols represent the dominant uncertainty in radiative forcing, partly because they present a very high spatio-temporal variability. In this context, satellite observations may offer a global and continuous observation at high resolution. In particular, remote sensing in the thermal infrared has several advantages: observations are available both for daytime and nighttime, dust characterization is possible over desert and, even more important, vertical sounders allow retrieving dust layer mean altitude. In this study, observations from nearly 9 years of Metop-A/IASI, as well as 3 years of Metop-B/IASI are interpreted in terms of dust aerosol properties (AOD and mean altitude). The method is based on a “Look-Up-Table” (LUT) approach, where all radiative transfer computation is performed once for all and “off-line”, for a large selection of atmospheric situations, of observing conditions, of surface characteristics (in particular the surface emissivity and temperature), and different aerosol refractive index models. The inversion scheme follows two main steps: first, determination of the observed atmospheric thermodynamic situation, second, simultaneous retrieval of the 10µm coarse-mode AOD, the mean altitude and surface temperature. The method is here applied to the latitude band 60S-60N, over sea and over land, at daily scale daytime and nighttime, and at the satellite pixel resolution (12 km at nadir). 

Resulting dust AOD are compared with AERONET AOD ground-based measurements and mean aerosol layer altitude obtained s compared at local scale with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP/CALIPSO) aerosol altitude.

We report on the climatological acetone distribution and seasonal variations in the upper troposphere and lower stratosphere of the northern midlatitudes, derived from observations by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) onboard SCISAT. The acetone profiles retrieved from 5 to ~20 km cover the period from January 2004 to September 2010. The 1s statistical fitting errors are typically ~ 5 – 20 % within the upper troposphere (UT), increasing in the lower stratosphere (LS) with decreasing acetone. The systematic errors range between 15 and 20 %. The largest UT acetone mixing ratios (~1200 ppt on average in July over Siberia) are observed in summer in the northern mid- and high latitudes. Mixing ratios are larger over continental regions than over the ocean. Comparisons with airborne measurements available in the literature point towards an underestimation in acetone retrieved from ACE-FTS by 10 to 50%. The largest differences occur primarily in winter and for the background values. This underestimation is attributed to the complexity of the spectral region used for the retrieval. The annual cycle of acetone for the 30-70°N midlatitude band shows a maximum during summer, reflecting the annual cycle of the primary terrestrial biogenic source of acetone. By comparison with ACE-FTS, the LMDz-INCA global climate-chemistry model systematically overestimates acetone mixing ratios lower than 400 ppt. This overestimation is thus generalized for the lower stratosphere, the Tropics and beyond 70°N for the upper troposphere. In contrast, in the upper troposphere of the 30°N-70°N region, where the acetone levels are the highest (> 450 ppt on average), the model-observation differences are in the range of the observation uncertainty. However, in this region, the model fails to capture the annual cycle of acetone, culminating in July. A seasonal cycle can only be obtained by considering high biogenic emissions but this cycle is shifted towards autumn, likely indicating an underestimation of the chemical destruction in the northern high latitudes.

Important progresses in the field of atmospheric ozone sounding from space have been accomplished during the last decade. The lower troposphere is now available from IASI (Infrared Atmospheric Sounding Interferometer) with a maximum of sensitivity between 3 and 4 km. We use satellite observations from IASI on board the MetOp satellites to evaluate the monthly variability and the short-term trends of tropospheric ozone over East Asia for the period 2008-2015. The availability of two semi-independent columns of ozone from the surface up to 12 km allow ones to derive ozone trends for the lower (surface to 6 km a.s.l) and the upper troposphere (6 to 12 km a.s.l). Monthly variations show a maximum in late spring/early summer (May, June) in the lower troposphere. Short-term trends are calculated from deseasonalized monthly variations. Preliminary results show a negative trend of about -0.50%/yr in the lower troposphere whereas a positive trend (~ +0.7%/yr) is observed in the upper troposphere. If the negative trend appears to be significant in the lower troposphere, the positive trend in the upper troposphere remains poorly significant over such a short period. The consistency between the two in-flight IASI instruments aboard MetOp-A and MetOp-B is discussed. The IASI derived trends are compared to independent in situ observations provided by ozone sounding at different sites of East Asia for evaluation. The different processes driving the variability and short-term trends of lower tropospheric ozone during the 2008-2015 period are discussed.

The European Space Agency (ESA) in the context of its “Space and Energy” cross-agency theme of activities, has supported the analysis of a carbon dioxide and methane point source detection concept, aiming to monitor regularly individual anthropogenic point sources, and natural sources of GHG. This paper provides an overview of the context, presents the results of user needs analysis and the description of the mission objectives. The mission concept is described, including the results of a preliminary feasibility analysis with the technical, programmatic and user value estimate of a potential instrument/satellite. The level 1 performance requirements are presented, and the expected performances in terms of level 2/level 3 products (GHG atmospheric content and their spatial gradients associated with atmospheric signatures of anthropogenic GHG sources) and level 4 products (GHG anthropogenic surface fluxes) are discussed.

 

As a first important asset of the approach, the work has been initiated by the analysis of the user needs, through a series of literature survey, interviews of actors in the energy sectors, and dedicated workshops. This allowed to identify user needs of remote sensing data for monitoring carbon dioxide point sources and for detecting methane emissions, and to quantify and prioritize needs for deriving mission requirements. The mission objectives are derived from this analysis: detecting and quantifying natural and anthropogenic surface fluxes of CH₄ and CO₂ at local scales for:

Anthropogenic emissions of CH₄ and CO₂ from power plants, combustion devices, coal mines, and network infrastructures ;

Emissions of CH₄ from natural methane emissions and leak assessment for methanisation plants.

The proposed mission strategy aims at measuring the space/time structures of the column average mixing ratio XCO₂ and XCH₄ in the atmosphere with high quality: high horizontal resolution (1 km); imaging of sufficiently large areas (typically 20 km x 20 km) for measuring spatial gradients and capturing the concentration distribution (plume) related to point source emissions; high spectral and radiometric performances (SNR of 300 to 350 @ 0.4 cm^-1 spectral resolution) aiming to achieve high accuracy in the atmospheric content of the two target gases (goal for XCO₂ : random error: 1 ppm, systematic error: 0.2 – 0.5 ppm; goal for XCH₄: random error : 5 - 10 ppb; systematic error : 1 - 3 ppb). It is proposed to develop an instrument with these state-of-the-art capabilities on a low Earth orbit (LEO) platform, with a reduced mass/reduced power budget.

In order to achieve these challenging requirements, a dedicated/innovative strategy of coverage and pointing is proposed, ensuring global access to the main anthropogenic emission targets every 5 days (for one satellite) or every day (for a constellation), and proposing an intelligent selection of observed cloud-free scenes using a companion imager.

Specific strategies and algorithms are designed for L2/L3 products retrieval. A preliminary analysis of the expected performances, and how these performances fit with the final services and user needs, will be discussed.

GOMOS, which flew onboard Envisat, is a pioneering experience based on the principle of stellar occultation. The retrieval of atmospheric quantities from the weak star signal, altered during its propagation through the atmosphere by scintillation and other refractive effects, is particularly challenging, and the quality of the retrieved profiles depends on several quantities such as the occultation and star properties.

A dedicated algorithm, IPF, was developed for the retrieval of trace gases and of aerosols. Although it provided effective extinctions at the nominal wavelength of 500 nm, this algorithm showed quite poor performances to describe the spectral features of the stratospheric extinction at other wavelengths in the GOMOS spectral range.

In order to address the problem of limited performances of the IPF operational retrieval algorithm in terms of aerosol retrieval, an alternative algorithm called AerGom has been developed with as main objective the optimization of the stratospheric aerosol component, and more particularly an improved derivation of its spectral dependence.

So far, the emphasis was set on the quality of the aerosol extinction, main target product for the retrieval. But AerGom also retrieves trace gases such as ozone, NO2 and NO3. Until now, little attention was paid to the investigation of the gas profiles retrieved by AerGom. However, retrieved profiles of trace gases and aerosols are obviously closely related, and a bad attribution of one species leads to a bad estimation of the other ones.

The recent developments aiming at solving the problems encountered in the spectral characterization of the aerosol extinction retrieved by AerGom, appeared also to result in increased performances in the gas retrieval.

This work presents an overview of the trace gases products provided by AerGom, and shows how algorithm developments initially aimed at improving the aerosol compound, also leads to a better quality of the retrieved gas products.

The objective of this topic is to put new facts into understanding of the coupling processes in the atmosphere-ionosphere system during the tropical cyclone (TC) action. Advanced international investigations of the correlation between tropical cyclones (TCs) and the ionosphere are connected with extreme difficulties of proving the action of possible mechanisms of TC effect on the ionosphere. TC are the greatest troposphere hazard. There are two of the possible “TC-Ionosphere” mechanisms - the Gravity Waves and the electric. GWs generated at tropospheric altitudes propagate to the F-region. Middle atmospheric dynamics, and particularly atmospheric waves, play a leading role in determining the variability of the atmosphere-ionosphere system. GWs generated from storms break near 100 km and produce secondary waves that continue to propagate upward GWs modulate the E-region plasma producing polarization fields that map to F-region altitudes. Strong convection cells produce a wide spectrum of GWs. GWs increase in amplitude within creasing altitude and may become unstable. Only waves propagating at the certain angles and with the correct amplitude can reach thermospheric altitudes. Once in the thermosphere, only those waves oriented to the magnetic field in a particular manner may produce ionospheric disturbances (dr.Rebecca Bishop, PSL/SSAL, 30 March 2012) An effect of external electric currents on the global atmosphere-ionosphere electric circuit may be one of possible mechanisms of interaction between atmospheric and ionospheric components. External currents with a horizontal scale of about one hundred of kms may be related to the vertical large - scale convection of the cloudy atmosphere in the zone of a TC and to the charge separation in this region. The electric field disturbance arises due to perturbation in the atmosphere – ionosphere electric circuit generated by the upward transport of charged water drops and aerosols in TC convection zone (Sorokin et al, 2005).

In this paper authors analyze the ionospheric data above Australia for last 5 years.

To investigate the potential that spacecraft constellations and formations present for Earth Observation, three ESA "Earth Observation Sentinel Convoy" studies are currently underway as part of the Support to Science Element (STSE) of the Earth Observation Envelope Programme (EOEP) of the European Space Agency (ESA). These studies aim to identify scientific and operational needs that would benefit from additional in-orbit support in three themed domains: ‘Ocean and Ice’, ‘Land’ and ‘Atmosphere’. The studies also intend to identify and develop cost-effective mission concepts that can meet these needs by flying in convoy with the European operational missions, such as MetOp Second Generation (SG). This paper provides an overview of the progress made on the theme ‘Atmosphere’. User needs and identified scientific gaps are outlined and to address these gaps the selected mission concept for further feasibility study is briefly described. To date, mesoscale winds, clouds and circulation are not well exploited in global NWP and climate models and phenomena of turbulence and convection are not explicitly represented in these models. These phenomena are however initiating atmospheric dynamics and are the basis of the interaction of the troposphere with the surface and stratosphere. The geometric Clouds Motion Winds (gCMW) concept targets the measurement of height-resolved wind fields exploiting the effect of parallax. A multi-angle imaging spectro-radiometer (cf. MISR) is targeted for providing cloud top heights and height-resolved wind, vertical motion, aerosol and cloud structures using a multi-angle imager and geometric optics. Enhanced performance with respect to earlier flown missions may be achieved by 1) launching a tandem of gCMW satellites, e.g., one leading and one following MetOp-SG, 2) allowing night-time measurements by using infrared channels and 3) obtaining winds at several heights by using different visible and infrared frequency channels. This information would greatly complement the MetOp instruments to vertically resolve dynamical structures. The MetOp-SG imagers and sounders would benefit from improved height assignment and cloud information, respectively. To optimise the determination of height and motion from the images the temporal co-registration between the convoy and MetOp-SG spacecraft should be only a few minutes, where the MetOp-SG nadir view should match fore or aft views of the convoy MISR satellites.

In this contribution, we present an open access stand-alone software tool developed in Java programming language which allows performing basic, yet of key importance, pre-processing steps to the SEVIRI operationally distributed products. The tool makes use of present day multicore processors, being able to process fast very large datasets, making it also suitable to be used in High Performance Computing (HPC) environment.

The practical usefulness of the software tool is demonstrated using as example herein the SEVIRI evapotranspiration ET) product. For this product, we further demonstrate how its robust validation can be conducted using our tool and HPC facilities of Wales, provided that concurrent reference observations are available from ground measurements. For this purpose we used in-situ data acquired at different European ecosystems from the CarboEurope ground observational network during year 2011.

 

Our work is significant to the SEVIRI users' community and also very timely given that, to our knowledge, no similar tool is freely available at present. Use of this software tool aims at supporting the wider dissemination and implementation of SEVIRI operational products, making a significant contribution to weather forecasting and global climate monitoring.

 

Urban air quality is affected by city pollution and especially aerosols which have significant negative health effects on population. The adverse health effects from particulate matter PM pollution, especially with aerodynamic diameter ≤2.5 μm PM2.5 must be considered in developing policies to improve air quality. Epidemiologic studies demonstrated that exposure to ambient particulate matter PM is associated with increased morbidity and mortality, particularly associated with cardiopulmonary disease and asthma of which children are most exposed for the rapid increase of asthma disease. Since PM is produced by various anthropogenic and natural sources with different atmospheric residence times, it has a high spatio-temporal variability, which is very important for epidemiological time-series studies.

An allergic reaction to different allergens is a major contributor to asthma in children living in urban environment, but new research suggests that the allergies are just one part of a more complex story. Very early exposure to certain components of air pollution can increase the risk of developing of different allergies by age 7. The epidemiological research on the mutagenic effects of airborne particulate matter pointed their capability to reach deep lung regions, being vehicles of toxic substances. In the recent years, ambient air pollution concentrations have been reduced Europe-wide by emission controls and fuel replacement. These measures are of particular interest for the regulating agencies as well as for the regulated entities. In spite ofair quality improvements recorded in Bucharest metropolitan area in Romania after 1990 year, still are recorded high levels of particulate matter (PM2.5 and PM10) and other pollutants concentrations. The present study attempts to retrieve the aerosol load in terms of aerosol optical depth (AOD) related to air quality in the Bucharest metropolitan area. For the purpose, both satellite remote sensing and ground-based measurements were used in conjunction with model applications. Satellite data of moderate spatial resolution (MODIS and MERIS) were used to retrieve the aerosol optical depth (AOD) over the urban area of Bucharest. MODIS products were obtained at a horizontal resolution of 10 by 10 centered over Bucharest, while the differential textural analysis (DTA) code was applied to MERIS images to retrieve relative-to-reference AOD with a resolution of 260m by 290m. In-situ monitoring data for particle matter PM2.5 and PM10 concentrations have been provided by Bucharest Air Pollution Network over 2011-2012 periods. Both in-situ monitoring data as well as time-series satellite data methods are important and complementary. Is presented a spatio-temporal analysis of the aerosol concentrations in relation with meteorological parameters in two size fractions (PM10 and PM2.5) and Air Qualiy Index and possible health effects on children’s asthma. It was found that PM2.5 and PM10 aerosols exhibit their highest concentration mostly in the central part of the town mainly due to road traffic as well as in the industrialized parts outside of city’s centre. Pediatric asthma can be managed through medications prescribed by a healthcare provider, but the most important aspect is to avoid urban locations with high air pollution concentrations of air particles and allergens.

 

The impact of intensive dust outbreaks from the African continent on the marine primary production of the Mediterranean sea is here investigated using MODIS satellite observations of atmospheric aerosol optical depth and chlorophyll-a in the seawater.  Dust outbreak episodes in the area are detected based on aerosol relevant satellite observations over a 12-year period from 2003 to 2014. For a total of 167 identified episodes, correlations between aerosol optical depth and chlorophyll-a are investigated both on regional and on a pixel by pixel basis as well as for simultaneous or time-lagged satellite observations. The identified co-variations are thoroughly discussed in view of the impact of nutrient atmospheric deposition on the Mediterranean Sea ecosystem.

The ADM-Aeolus will carry first Doppler lidar in space and launch in the near future. A mobile wind lidar was developed by Ocean Remote Sensing Institute of Ocean University of China, this lidar will be able to validate ADM-Aeolus wind, cloud and aerosol data products under format of cooperate with DLR(Dragon 3 ID 10532).

Recently, we develop a compact shipborne Mie lidar for measuring cloud and aerosol data on the ocean. An experiment was conducted around Indian Ocean, E80°—E105°,S5°—N10° were covered to measure the troposphere marine aerosols using this system from March 5 to May 13,2013 and in the offshore area of Qingdao also to measure the troposphere marine aerosols from May 25 to 31 and August 29 to September 2,2015. 36°N—35°N,120°E—121°E were covered. The value of Lidar ratio in the experimental area and time is determined as 24sr using aerosol optical depth（AOD）measurements form CALIPSO Lidar level-2 profile data products as constrain. Then the Lidar signal is processed by the Fernald method to calculate the aerosol extinction coefficient profile and optical depth below 15km altitude above the sea surface. The mean value of AOD is 0.16 in the absence of any cloud. Compare compact lidar data with data of sun-photometer MICROTOPS II and MODIS satellite, basically consistent . This marine experiment has proved that the system is portable, reliable and available for operating on a ship, and can measure the marine atmospheric aerosols and clouds effectively and continuously with high spatial and temporal resolution. We hope this shipborne lidar will be able to validate also AOM could and aerosol data.

 

Emissions of volcanic gases and particles can have profound impacts on terrestrial environment, atmospheric composition, climate forcing, and then on human health at various temporal and spatial scales. Volcanic emissions have been identified as one of the largest sources of uncertainty in our understanding of recent climate change trends. In particular, a primary role is acted by sulphur dioxide emission due to its conversion to volcanic sulphate aerosol via atmospheric oxidation. Aerosols may play a key role in the radiative budget and then in photochemistry and tropospheric composition. Mt.Etna is one of the most prodigious and persistent emitters of gasses and particles on Earth, accounting for about 10% of global average volcanic emission of CO₂ and SO₂. Its sulphur emissions stand for 0.7 × 10⁶ t S/yr⁹ and then about 10 times bigger than anthropogenic sulphur emissions in the Mediterranean area.

Centrepiece of the SMED project is to advance the understanding of volcanogenic sulphur dioxide and sulphate aerosol particles dispersion and radiative impact on the downwind  Mediterranean region by an integrated approach between ground- and space-based observations and modelling. Research is addressed by exploring the potential relationship between proximal SO₂ flux and aerosol measured remotely in the volcanic plume of Mt. Etna between 2000 and 2014 and distal aerosol ground-based measurements in Lampedusa, Greece, and Malta from AERONET network. Ground data are combined with satellite multispectral polar and geostationary imagers able to detect and retrieve volcanic ash and SO₂. The high repetition time of SEVIRI (15 minutes) will ensure the potential opportunity to follow the entire evolution of the volcanic cloud, while, the higher spatial resolution of MODIS (1x1 km²), are exploited for investigating the probability to retrieve volcanic SO₂ abundances from passive degassing. Ground and space observations are complemented with atmospheric Lagrangian model (FLEXPART) for inspecting the transport and dispersion of volcanic plume over the Mediterranean region and the radiative transfer model UVSPEC for inspecting radiative forcing of volcanic sulphates over the Mediterranean region.

Preliminary results exploring the Central-Southern Mediterranean, reveal that thought only 2 -7% of Mt.Etna’s volcanic clouds disperses over this region, volcanic impact might be relevant in both SO₂ abundances and sulphate-volcanogenic derived aerosol.  

In recent years, running trends analysis (RTA) has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that RTA might be a useful descriptive tool, but, despite its general use in applied research, precisely what it reveals about the underlying time series is unclear and, as a result, its interpretation is unclear too. This work contributes to such interpretation in two ways: 1) an explicit formula is obtained for the set of time series with a given series of running trends, making it possible to show that running trends, alone, perform very poorly as summary statistics for time series analysis; and 2) an equivalence is established between RTA and the estimation of a (possibly nonlinear) trend component of the underlying time series using a weighted moving average filter. Such equivalence provides a solid ground for RTA implementation and interpretation/validation. 

CNES, the French space agency, and DLR, the German Space Administration, in order to strengthen their cooperation in space activities, decided to develop an Earth-Observation mission, dedicated to methane (CH₄) monitoring. The MERLIN (MEthane Remote sensing LIdar missioN) mission is based on a small satellite for space-based measurement of spatial and temporal gradients of atmospheric methane columns on a global scale. The MERLIN PLDP performs the PayLoad Data Processing which ensures availability of science data according to science requirements. The mission data products are divided into different levels (from 0 to 3), and distributed in accordance with the data policy defined by the parties which contribute to the mission.

 

The PLDP which will be installed in the Toulouse Space Centre, will produce, archive and disseminate a little more than half Petabytes of data. For the data production, the mission data centre will lean on the mutualized services offered by the CNES IT department, by limiting at the most, the specific developments. The data storage will lean on HSM technology and will allow to put “on line” levels 1, 2 and 3 products during ten years and this, few times after generation. A quite particular attention is given since the project B phase to the data preservation beyond the ten years required in the mission user requirements. The various data or products generated will be given to the scientific community according to their level end sensibility. A MERLIN web site will be created for the products dissemination; it will also be the showcase of the MERLIN project inside the world wide scientific community. This web site will be integrated inside the atmospheric pole (Aeris) set up by the CNES. The methane data generated will be the first data with full world coverage put at the disposal of the scientific community.

SENTINEL 4 is an imaging UV-VIS-NIR spectrometer, developed by Airbus DS under ESA contract in the frame of the joint EU/ESA COPERNICUS program. The mission objective is the operational monitoring of trace gas concentrations for atmospheric chemistry and climate applications. To this end SENTINEL 4 will provide accurate measurements of key atmospheric constituents such as ozone, nitrogen dioxide, sulfur dioxide, methane, and aerosol properties.

In the line of UVN spectrometers with space heritage  and under development, SENTINEL 4 is unique in being the first geostationary UVN mission. The SENTINEL 4 space segment will embark on EUMETSAT's Meteosat Third Generation Sounder satellite (MTG-S), sharing its platform with the MTG IRS instrument. For the period of time between 2021 and 2034 SENTINEL 4 will provide coverage of Europe and adjacent regions with a repeat cycle of 60 minutes and a spatial resolution of 8x8 km (at reference point in Europe). This spatial coverage is achieved by push-broom continuous E/W scanning of a slit with 4° N/S field-of-view over an E/W field-of-regard of about 11°.

The SENTINEL 4 spectrometer will acquire continuous spectra of Earth radiance covering the UV (305-400 nm), VIS (400-500 nm) and NIR (750-775 nm), with spectral resolution of 0.5 nm in the UV-VIS and 0.12 nm in the NIR.

Further key design-driving performances include: Low sensitivity to polarization; Low level of spectral features; Low straylight; Complex & highly accurate calibration & correction; High radiometric accuracy; Highly challenging geometric accuracies (scan accuracies, spatial co-registration, etc.).

This paper gives an overview of the SENTINEL 4 system architecture, its design & development status, current performances and the key technological challenges.