Validation and homogenization of cloud property retrievals for RMIB GERB/SEVIRI scene identification

Abstract

The Geostationary Earth Radiation Budget (GERB) instrument has been launched this summer together with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) satellite. This broadband radiometer will aim to deliver near real-time estimates of the top of the atmosphere (TOA) radiative fluxes at the high temporal resolution due to the geostationary orbit. In order to infer these fluxes, a radiance-to-flux conversion based on Clouds and the Earth's Radiant Energy System (CERES) angular dependency models (ADMs) need to be performed on measured radiances. Due to the stratification of these ADMs according to some CERES scene identification (SI) features such as cloud optical depth and cloud fraction, the GERB ground segment must include some SI on SEVIRI data which mimic as close as possible the one from CERES in order to select the proper ADM. In this paper, we briefly present the method we used to retrieve cloud optical depth and cloud fraction on footprints made of several imager pixels. We then compare the retrieval of both features on the same targets using nearly time-simultaneous Meteosat-7 imager and CERES Single Satellite Footprint (SSF) data. The targets are defined as CERES radiometer footprints. We investigate the possible discrepancies between the two datasets according to surface type and, if they exist, suggest some strategies to homogenize GERB retrievals based on CERES ones.