Aerosols / Clouds 1

Within the ESA Climate Change Initiative (CCI) project Aerosol_cci (Phase 1: 2010 –2014; Phase 2: 2014-2017) intensive work has been conducted to improve algorithms for the retrieval  of aerosol information from European sensors ATSR-2 (ERS-2), AATSR (3 algorithms), MERIS (3 algorithms), synergetic AATSR/SCIAMACHY, GOMOS (all on ENVISAT), IASI (on METOP), PARASOL and OMI (EOS-Aura) (both part of NASA’s A-Train). Whereas OMI and GOMOS are used to derive absorbing aerosol index and stratospheric extinction profiles, respectively, Aerosol Optical Depth (AOD), Fine-mode AOD and Ångström coefficient are retrieved from the other sensors, while the infrared IASI retrievals deliver mineral dust AOD. In addition also a per-pixel uncertainty is provided and validated. The algorithms are then used to produce full-mission time series covering 10 (stratospheric extinction), 17 (AOD) and 35(AAI) years. Using AERONET sun photometer data as the common ground-truth both ‘traditional’ statistical techniques and a ‘scoring’ technique based on spatial and temporal correlations were applied for thorough L2 and L3 product validation. The iterative cooperation between the project partners, including retrieval teams, independent validation teams and core users has resulted in a strong improvement of most algorithms. Several user case studies (data assimilation, trend analysis, radiative forcing, stratospheric climate-chemistry interaction, aerosol cloud interaction) are analyzing the potential usefulness and limitations of these datasets. Several retrieval inter-comparisons, so-called Round Robin exercise, have been conducted to assess the sensitivities of different algorithms for the same sensor (AATSR, IASI) or variable (absorption, layer height) and to reveal the major uncertainties, specific strengths and weaknesses of the different retrieval approaches under varying environmental conditions.

The presentation will summarize and discuss the status of the Aerosol_cci efforts to produce consistent aerosol time series and characterize and document appropriately to users their potential benefits and limitations. An outlook overview of future potential developments will be deduced from the current status.

Aerosol products from polar-orbiting satellite sensors represent a significant improvement over those from other satellite imagers. However, polar-orbiting satellites are restricted to overpasses at a fixed local time, and thus cannot resolve the diurnal cycle and temporal evolution of aerosols. The geostationary satellites with high temporal resolution are capable of capturing the aerosol diurnal variation. This capability can be used to for air quality and dust transport studies and to further reduce the uncertainties in the current aerosol forcing estimations caused by high temporal variations of aerosol properties, thereby playing a role complementary to global aerosol optical depth (AOD) retrievals from polar orbiting satellites.

ESA is developing five new missions called Sentinels specifically for the operational needs of the GMES programme.  Sentinel-4 is a payload that will be embarked upon a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit scheduled to be launched in 2019. Sentinel-4 is dedicated to atmospheric monitoring.

In this article, we present a new algorithm named Land Aerosol property and Bidirectional reflectance Inversion by Time Series technique and apply it on MSG/SEVIRI data. Aerosol type, surface reflectance and AOD can be determined simultaneously. A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that > 75% of the MSG-retrieved AOD at 0.6 and 0.8µm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15τ and a high correlation coefficient (R > 0.86). The AOD datasets derived using the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially useful for air pollution and dust storm monitoring using SEVIRI observations.

The ESA-SEOM 'Advanced Clouds, Aerosols and WAter vapour products for Sentinel-3/OLCI' CAWA project aims to the development and improvement of advanced atmospheric retrieval algorithms for the Sentinel-3/OLCI mission, and will be prepared by using Envisat/MERIS and Aqua/MODIS datasets. Firstly, results of a sensor comprehensive and consistent 1D-Var water vapour algorithm applied to the MERIS, MODIS and upcoming OLCI measurements will be presented. Secondly, an innovative and consistent cloud top pressure 1D-Var procedure as defined for MERIS as well as all three OLCI O2 A-band channels will be discussed, based on radiative transfer simulations and applications to MERIS data. Thirdly, the challenging and innovative GRASP algorithm for the retrieval of aerosols and surface properties has been applied to MERIS data, showing its advantage in comparison to conventional aerosol retrieval methods. The intention of the CAWA team is to establish new and improved procedures to estimate atmospheric properties, which also improve the retrieval of land and ocean properties. The algorithms will be implemented in the free available ESA Sentinel 3 toolbox.

The Ring effect describes the so-called ‘filling-in’ of solar Fraunhofer lines in the spectra of scattered sun light compared to direct sun light observations. It was first observed by Shefov (1959) and Grainger and Ring (1962). Observations of the Ring effect can be used to investigate details of the atmospheric radiative transfer. The filling-in of Fraunhofer lines depends in particular on the presence and properties of clouds and aerosols. The Ring effect can in principle be analysed throughout the whole UV/visible wavelength range, but the strongest signals are usually found in the UV. Several algorithms for cloud properties from Ring effect observations were developed in recent years and successfully applied to satellite observations from TOMS, GOME-1/2, SCIAMACHY and OMI. In this study we extend the application of satellite Ring effect observations to the retrieval of information on tropospheric aerosols. In contrast to observations of absorptions of the oxygen molecule O₂ and most absorption bands of the oxygen dimer O₄, Ring effect retrievals in the UV spectral range show only a rather weak dependence on surface albedo. Thus the presence of aerosols usually leads to a decrease of the Ring effect (depending on layer height), because aerosols shield possible Raman scattering events on air molecules inside and below the aerosol layer. We present case studies of Ring effect observations from the OMI instrument. This instrument is particularly well suited for Ring effect analyses because of its rather small ground pixel size (minimising the interference with clouds) and because of its low polarisation sensitivity. We investigate the effects of aerosols on the Ring effect analysis in different wavelength ranges for different aerosol optical depths and layer heights.

The daily evolution of the three-dimensional (3D) structure of a major Sahran dust outbreak over the Sahara is observed from space for the first time, using new aerosol retrievals derived from IASI (Infrared Atmospheric Sounding Interferometer). This dust event was initiated by a suite of cold-pools generated by mesocale convective systems over West Africa in June 2011. We have used a novel auto-adaptive Tikhonov-Philips-type approach called AEROIASI to retrieve vertical profiles of dust extinction coefficient at 10 μm for most cloud-free IASI pixels, both over land (including bright desert surfaces) and ocean.

The dust vertical distribution derived from AEROIASI is shown to agree remarkably well with along-track transects of CALIOP space-borne lidar vertical profiles as well as with aerosol optical depth derived from AERONET sun photometer measurements over West Africa. A comparison is as well made with airbone lidar and in situ measurements over the Sahara performed in the framework of the FENNEC field campaign. We also compare AEROIASI with several satellite retrievals available over the Sahara (MODIS-Deepblue, OMI, SEVIRI). 

AEROIASI allows the daily characterization of the 3D transport pathways of a major dust plumes across the Sahara. We focus our study on the role of the mesoscale atmospheric circulation over the Sahara and the Saharan atmospheric boundary layer on the 3D distribution of Saharan dust.