Session: Atmosphere I

The presentation discusses the potential of applying recently developed GRASP algorithm for retrieving properties of aerosol and underlying surface from Sentine-3 observations. The GRASP (Generalized Retrieval of Aerosol and Surface Properties) algorithm described by Dubovik et al. (2011, 2014) derives an extended set of aerosol parameters including detailed particle size distribution, spectral refractive index, single scattering albedo and the fraction of non-spherical particles. Over land GRASP simultaneously retrieves properties of both aerosol and underlying surface. The remarkable performance of the algorithm for retrieval of detailed aerosol properties was illustrated from multi-viewing polarimeter POLDER/PARASOL observations. Here we show that applying such complex algorithm as GRASP can be also beneficial for processing Sentinel – 3 data.  Indeed, GRASP is designed to retrieve extended set of atmospheric parameters even from the sensors that do not have multi-angular or polarimetric capabilities.  Specifically it uses the new multi-pixel concept - a simultaneous fitting of a large group of pixels with additional constraints limiting the time variability of surface properties and spatial variability of aerosol properties. This principle provides a possibility to improve retrieval for multiple observations even if the observations are not exactly co-incident or co-located.

Here we present the results of our efforts to apply the GRASP for deriving the aerosol properties from OLCI observations. The results of comprehensive sensitivity tests, as well as results obtained from real MERIS data will be presented. In addition, we explore possibilities of implementing synergetic retrieval by inverting a combination of OLCI data with observations by SLSTR or by the instruments from other than Sentinel-3 platforms.  

Dubovik, O., M. Herman, A. Holdak, T. Lapyonok, D. Tanré, J. L. Deuzé, F. Ducos, A. Sinyuk, and A. Lopatin, “Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations”, Atmos. Meas. Tech., 4, 975-1018, 2011.

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 .

The MicroWave Radiometer (MWR) flown on Envisat, ERS-1 and ERS-2 provides a nearly uninterrupted time series of microwave observations over a period of almost 21 years between 1991 and 2012. This dataset is complementary to other microwave datasets. Despite its nadir-only coverage it provides an opportunity to independently provide constraints on total column water vapor (TCWV) and cloud liquid water path (LWP).

Here we report on our efforts towards a fully inter-calibrated and validated physical retrieval of TCWV and LWP for MWR. We will address issues related to satellite inter-calibration, homogeneity of the derived time series of brightness temperatures, observation-simulation biases, and provide first results of fully physical retrievals. An outlook on planned activities will be given and the importance of the results in light of the upcoming Sentinel-3 mission will be discussed. 

Within the ESA CCI-Aerosol project we developed an innovative retrieval method to quantify aerosol absorption utilizing the ability of the dual view sensor Advanced Along-Track Scanning Radiometer (AATSR).

The difficulty in estimating the aerosol absorption, using passive radiometric measurements, is to disentangle the aerosol extinction into its scatter and absorption properties. In our algorithm the derivation of the optical properties is done by utilizing the different effects of aerosols on the signal over a highly and poorly reflecting surface under two different viewing geometries provided by AATSR/SLSTR. This is true for areas above water surfaces where one of the two observation geometries is strongly sun glint effected and the other is not. In areas were the gradient between the glint and off glint observation is strong enough we can see that an absorbing aerosol has a much stronger dimming effect on the signal above the bright surface compared to a mainly scattering aerosol with the same AOD. Whereby in the off glint observation the absorbing aerosol has a much lower brightening effect on the signal compared to the scattering aerosol layer.                                                                                                                       As the sun glint is used as a lower boundary condition in the presented method an accurate determination of the ocean surface is needed. In our method we use the 3 thermal channels to estimate the amount of reflected sunlight due to glint in 3.7 micrometer channel. The determined sun glint in the 3.7 micrometer channel is further used to derive a parameter describing the water surface roughness. Therefore we used full radiative transfer calculations where the optical properties for a wind roughed sea surface are based on Cox and Munk. Herein the glint can be described as a poor function of observing geometry, refractive index and sea surface roughness which is true as we assume a black water body in the analysed spectral range. According to this information the glint can then be transferred to the visible channels of the instrument.                                                                                       Studies on the applicability of the method on AATSR and MERIS show a strong dependency on the sun position. Depending on the season and zonal position along the AATSR swath the method can be applied up to about two-third of the swath width.                                                                                                                                                        The change in the observation geometry from AATSR to SLSTR, to avoid sun glint effects, has therefore a direct effect on the applicability of the algorithm on the future instruments. The presentation will be focused on the results of sensitivity studies and adaptability of the algorithm to the new instruments SLSTR and OLCI on sentinel-3.

The FAME­C (FUB AATSR­MERIS 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 well­established cloud typing algorithm. Cloud products are retrieved using measurements from the Advanced Along­Track Scanning Radiometer (AATSR) and the MEdium Resolution Imaging Spectrometer (MERIS), both mounted on the polar orbiting satellite ENVISAT.

A set of macro­physical, optical and micro­physical cloud properties are retrieved within FAME­C. AATSR measurements in the visible are used to derive daytime micro­physical 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 Oxygen­A absorption band.

In cloudy situations this average photon path length is mainly determined by the cloud top pressure, but also influenced by the cloud vertical extinction profile. Extensive sensitivity and validation studies have been performed investigating the sensitivity of the two independent cloud height retrievals to cloud vertical distribution by comparing the retrievals to observations from active instruments.

First efforts towards the adjustments of the FAME­C algorithm for measurements from the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments, both to be mounted on the follow­up mission Sentinel­3 satellite which is planned to be launched in mid 2015, are presented. OLCI will have three channels in the Oxygen­A absorption band, which means an increase in information on the cloud vertical distribution.

Simultaneous retrieval of aerosol and surface properties by means of inverse techniques based on a coupled atmosphere-surface radiative transfer model (CRTM) and optimal estimation can yield a considerable improvement in retrieval accuracy based on radiances measured by MERIS, MODIS, and similar instruments compared with traditional methods. There are uniqueness problems associated with photometric remote sensing measurements (like MERIS) that ignore polarization effects, and rely on measuring only the radiance. Use of polarization measurements is particularly important for absorbing aerosols over coastal waters as well as over bright targets such as snow and ice, where it has proved difficult to retrieve aerosol single-scattering albedo from radiance-only spectrometers such as MERIS and MODIS. We use a vector radiative transfer model for the coupled atmosphere-surface system in conjunction with an optimal estimation/Levenberg-Marquardt method to quantify how polarization measurements can be used to overcome the uniqueness problems associated with radiance-only retrieval of aerosol parameters. However, this study also indicates that even for existing instruments like MERIS and MODIS and future instrument like OLCI, that measure radiance-only, use of a polarized CRTM as a forward model in the optimal estimation can lead to significant enhancement of retrieval capabilities, and facilitate simultaneous retrieval of absorbing aerosols and marine parameters in turbid coastal environments.