Thesis of Marcos Herreras

Abstract : The understanding of the uncertainties in the retrieval of the aerosol and surface properties is very important for an adequate characterization of the processes that occur in the atmosphere. However, the reliable characterization of the error budget of the retrieval products is a very challenging aspect that currently remains not fully resolved in most remote sensing approaches. The level of uncertainties for the majority of the remote sensing products relies mostly on post-processing validations and inter comparisons with other data while the dynamic errors are rarely provided. This study describes, discusses and evaluates a concept realized in GRASP (Generalized Retrieval of Atmosphere and Surface Properties) algorithm for providing the dynamic estimates of uncertainties for retrieved parameters. The approach employs a rigorous concept of statistical optimization for estimating the effects of measurement uncertainties propagation to the retrieval results. The approach accounts for the effect of both random and systematic uncertainties in the initial data and provides error estimates both for directly retrieved parameters included in the retrieval state vector and for the characteristics derived from these parameters. The efficiency of the realized error estimation concept is extensively analyzed for GRASP applications for aerosol retrieval from ground-based observations by sun/sky photometer and lidar. The diverse aspects of the generations and evaluations of the error estimates are discussed and illustrated. The evaluation of the error estimates was realized using the series of comprehensive sensitivity tests when simulated sun/sky photometer measurements and lidar data are perturbed by random and systematic errors and inverted. The results of the retrievals and their error estimations obtained in the tests are analyzed and evaluated. The tests are conducted for the different observations of several types of aerosols including biomass burning, urban, dust and their mixtures. The study considers popular observations by AERONET sun/sky radiometer at 440, 675, 870 and 1020 nm and multi-wavelength elastic lidar at 355, 532 and 1064 nm. The sun/sky radiometer data are inverted alone
or together with lidar data. The analysis shows that the generated error estimates overall satisfactory of the uncertainties of different retrieved aerosol characteristics including aerosol size distribution, complex refractive index, single scattering albedo, lidar ratios, aerosol vertical profiles, etc. Also, the analysis shows that the main observed error dynamic agrees well with the errors tendencies commonly known from
the retrieval experience. For example, the serious retrieval accuracy limitations for all aerosol types are associated with the situations with low optical depth. Also, for observations of multi-component aerosol mixtures, the reliable characterization of each component is possible only in limited situations, for example from radiometric data obtained for low solar zenith angle observations or from a combination of radiometric
and lidar data. At the same time, total optical properties of aerosol mixtures tend to be always retrieved satisfactorily. In addition, the study includes the analysis of the detailed structure of correlation matrices for the retrieval errors of mono- and multi-component aerosols. The conducted analysis of error correlation appears to be a useful
approach for optimizing observations schemes and retrieval setups. The illustration of the developed approach application to real data is provided for co-located observations of sun/sky photometer and lidar over Buenos Aires. Furthermore, the preliminary results for utilizing the error estimates for the retrieval of aerosol from satellite data are provided. Keywords : atmospheric aerosol,error estimates,remote sensing