Bayesian cloud mask for GOES-SST operational products
Eileen Maria Maturi, NOAA/NESDIS, Camp Springs, MD; and A. Harris, C. J. Merchant, C. Old, and J. Mittaz
The NOAA Office of Satellite Data processing and Distribution is generating sea surface temperature (SST) retrievals on an operational basis from the GOES-11 and 12 satellite imagers using a new cloud masking methodology based on a probabilistic (Bayesian) approach. This new GOES SST Bayesian algorithm provides an estimate of the probability of cloud contamination for individual SST retrievals. The confidence level of the cloud detection is included as a separate variable in the product, allowing end-users the option of choosing the cloud threshold level to suit their requirements.
This new methodology applies Bayes' theorem to estimate the probability of a particular pixel being clear of cloud, given the satellite-observed brightness temperatures, a measure of local texture, and channel brightness temperatures calculated for the given location and view angle using NCEP GFS surface and upper air data and the CRTM fast radiative transfer model. The method is described in detail in a paper by Merchant et al. (2005). Comparisons of the original threshold cloud detection scheme with the new Bayesian method show a dramatic improvement in the coverage of the GOES-SST retrievals, particularly in oceanographically complex regions and areas of cold clear water. The Bayesian cloud mask is also being extended for use in the generation of SSTs from Meteosat-8 and MT-SAT 1R.
Session 5A, Satellite IIPS and Applications
Wednesday, 17 January 2007, 8:30 AM-10:00 AM, 216AB
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