P4.22
Geostationary Sea Surface Temperature Products (Current and Future)
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Poster PDF (118.0 kB)
A new cloud masking methodology based on a probabilistic (Bayesian) approach has been implemented for improved retrieval accuracy. This new GOES SST Bayesian algorithm estimates probability of cloud contamination for each SST retrieval, thus indicating the confidence level of the cloud detection which can, in turn, be related to retrieval accuracy.
The GOES-SST products generated from these algorithms include hourly regional sectors and 3-hourly hemispheric imagery, 24 hour merged composites and the new combined POES/GOES 10-km resolution SST analysis. Other GOES-SST related activities that are ongoing include the reprocessing of GVAR data back to 1994 (i.e. GOES-8) and Risk Reduction studies for the upcoming GOES-R program.
