Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 1. Theory



[1] An original method is presented in this paper for the joint retrieval of the mean daily total column aerosol optical depth and surface BRF from the daily accumulated Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) observations in the solar channels. The proposed algorithm is based on the optimal estimation (OE) theory, a one-dimensional variational retrieval scheme that seeks an optimal balance between information that can be derived from the observations, and the one that is derived from prior knowledge of the system. The forward radiative transfer model explicitly accounts for the surface anisotropy and its coupling with the atmosphere. The low rate of change in the surface reflectance is used to derive the prior information on the surface state variables. The reliable estimation of the measurement system error is one of the most critical aspects of the OE method as it strongly determines the likelihood of the solution. An important effort in the proposed method has thus been dedicated to this issue, where the actual radiometric performances of SEVIRI are dynamically taken into account.