Skip Top Navigation
only rendered in printing; banner identifies NOAA, NESDIS, and STAR organizations
STAR title banner
NOAA  |  NESDIS  |  STAR  |  SOCD  |  Intranet  |  Advanced Search

GOE-SST Products: Background


In December 2000 the GOES (Geostationary Operational Environmental Satellites) SST product became operational at the National Oceanic and Atmospheric Administration's Office of Satellite Data Processing and Distribution. The original operational GOES SST algorithms used for GOES-8 and -10 were based on a statistical.empirical algorithm similar to that used for AVHRR data (Wu et al. 1999). The initial operational algorithm of Wu et al. screened the data for cloud contamination using a series of threshold tests, combined with a retrieval algorithm derived by regressing satellite measurements versus in situ buoy measurements over a test period of 1996/97. The GOES SST algorithm required further development and revision with the launch (in July 2001) of GOES-12, which replaced GOES-8 in April 2003 as the operational GOES-East (E) satellite. Because the imager on GOES-12 no longer carried the 12-.m channel, the conventional split-window capability for daytime retrievals was eliminated. Collaborating with the University of Edinburgh, a new radiative transfer model (RTM) based SST algorithm since 2003 and a Baysian cloud-detection methodology since 2006 were implemented at the NOAA Office of Satellite Data Processing and Distribution (Maturia et al. 2008) to generate operational GOES SST products. This new SST retrievals system was applied in Japan.s Multifunctional Transport Satellite (MTSAT) in January 2008, and the Meteosat Second Generation Satellite in September 2008. The NOAA's generation of geostationary SST products is now moving toward near-global coverage.

Bayesian Cloud Mask

Detection and rejection of cloud interference is an important and often problematic step for SST products. We have adopted a probabilistic approach that replaces the traditional threshold-based cloud screening in operational SST products. This new methodology uses Bayes.s theorem to estimate the probability of a particular pixel being clear of cloud contamination. The estimate involves a comparison of satellite-observed brightness temperatures compared with those predicted for the given location and view angle using surface and upper-air data from the National Centers for Environmental Prediction.s (NCEP) Global Forecast System (GFS; using the 6-and 12-h forecast) and the Community Radiative Transfer Model (CRTM). The method is described in detail in a paper by Merchant et al. (2005).


The current SST algorithm is of the following form:

Where T is channel brightness temperature in Kelvin, i is the GOES imager channel number (i.e., 2, 4, 5), S is the fractional excess air mass, and the as are linear retrieval coefficients.

The GOES-11, GOES-12/13, MTSAT, and MSG algorithms all use this form for the RTM retrieval algorithm. GOES-12/13 requires prior application of sun glint and atmospheric scattering algorithms daytime (Merchant et al. (2008). The table below lists the operational coefficients from GOES-11 through -13 and MTSAT and MSG.

SST Products

The NOAA operational Geo-SST (GOES, MTSAT, MSG) products provide hourly, 3-hourly and 24 hour regional merged SST composites at 0.05o, Level 2 preprocessed product at every half hour for each hemisphere for GOES, every hour for MTSAT and every 15 minutes for MSG. A matchup database is generated for validation of the Geo-SST retrieval algorithms. The global drifting buoys in native satellite projection and resolution within half hour are matched with retrieved SST. The related satellite brightness temperature (BT), model BT and other information are also included in the matchup database.

Monitoring and Validation

The Goe-SST quality monitoring and validation system developed at NOAA/NESDIS/STAR, is designed for the quality control and quality assurance of the NOAA Geo-SST products. The system acquires Geo-SST products, generate statistics and graphics, and post in near real-time on NESDIS/STAR based monitoring and validation websites. The system is composed of SST monitoring and validation parts. The monitoring section includes SST and cloud mask from gridded product with 3 hours latency, daily SST and fronts with one day latency, and key parameters from L2P product with 6 hours latency. The statistics of retrieved SST minus buoy SST i.e. number of matches, bias and standard deviation by monthly and 5-days were computed and displayed in validation section.