Tuesday, 16 January 2007
Adapting operational GOES-SST algorithms to Meteosat Second Generation-SEVIRI for GOES-R Risk Reduction
Exhibit Hall C (Henry B. Gonzalez Convention Center)
The current operational GOES-SST RTM algorithm with the Bayesian cloud mask is being adapted to the Meteosat Second Generation (MSG)-SEVIRI data. While this will extend geostationary SST coverage eastwards from the area already covered by the operational GOES satellites, it also serves as a valuable risk reduction exercise for a physically-based GOES-R SST product from the Advanced Baseline Imager (ABI). The SEVIRI instrument has many characteristics (spectral channels, etc.) which make it a valuable proxy for the ABI. The physical SST retrieval methodology developed for the current GOES Imager requires certain tunings (mainly radiance bias correction) in order to perform in an optimal manner and similar techniques will need to be employed for the ABI. The extension from 5 (GOES Imager) to 12 channels (in the case of SEVIRI) is a significant first step in refining and testing the methodologies that will be required in the GOES-R era. Methods will be developed for RT bias corrections, including expected vs. observed brightness temperature distributions as modeled using NWP fields and RTM and cross-instrument comparisons of hyperspectral and broadband radiometer data. These can be tested using AIRS/MODIS and IASI/AVHRR combinations, as well as cross-platform comparisons (e.g. MSG/Aqua). The retrieval accuracy obtained by applying a probabilistic cloud screening methodology using the new Bayesian approach compared with the traditional threshold-based scheme will be evaluated. .
The above mentioned actives can lead to improvements to the CRTM. Propagation of errors (including cloud detection) through to SST analysis procedures and better error characterization for combining the MSG SST and MetOp SST products into a single analysis.