Sea Surface Temperature
SST is needed for many applications including monitoring climate variability, seasonal forecasting, operational weather and ocean forecasting, military and defense operations, validating or forcing ocean and atmospheric models, ecosystem assessment, tourism, and fisheries. Satellite retrievals of SST can be assimilated into climate, mesoscale atmospheric, and sea surface numerical models, which form the cornerstone of the operational ocean forecasting systems.
GOES-R will provide forecasters with a Sea Surface Temperature (SST) for each cloud-free pixel over water identified by the GOES-R ABI. The SST algorithm employed on GOES-R will use hybrid physical-regression retrieval in order to produce a more accurate product.The temperature of the ocean at depths on the order of 10 microns is retrieved by the SST product algorithm. Skin sea surface temperature (SST) will be retrieved using the fact that upwelling thermal infrared (TIR) radiation is sensitive to the temperature of the upper few micrometers of sea surface, referred to as "skin layer".
Improvements and Benefits
Knowledge of the SST can be beneficial for a large spectrum of operational applications that include: climate monitoring/forecasting, seasonal forecasting, operational weather and ocean forecasting, military and defense operations, validating or forcing ocean and atmospheric models, seas turtle tracking, coral bleach warnings and assessment, tourism, and commercial fisheries management.
How does it work? - Algorithm
Three versions of SST algorithms have been implemented: (1) the regression algorithm, based on split-window nonlinear SST (NLSSST) and/or multi-channel SST (MCSST); (2) the radiative transfer model (RTM) inversion algorithm, based on the optimal estimation (OE) technique; and (3) the hybrid algorithm, based on a combination of the above two approaches. The ACSPO system requires as its input optical and thermal infrared channels, navigation and observational/illumination geometry. Two of its major ancillary data sources are the global daily 0.25° and weekly 1 - Land and Ocean Data Products - reference Reynolds SST (OISST) fields, and 6-hour 1° National Centers for Environmental Prediction Global Forecast System (NCEP/GFS) atmospheric profiles. Ancillary GFS and OISST data are used as input to the fast Community Radiative Transfer Model (CRTM) to simulate clear-sky channel BTs. CRTM BTs are utilized for inversion and hybrid SST retrievals and QC of SST and BT. Web-based tools are being developed for near-real time (NRT) operational monitoring of the quality of SST and BT products and for calibration/validation (Cal/Val) of SST products. Currently, these tools are employed routinely with multiple AVHRR (NOAA 16-19) and METOP-A sensor data.
Example of the GOES-16 nighttime Sea Surface Temperature (SST) composite generated from ABI data on 27 October 2017
See the GOES-R ATBD page for all ATBDs.
How are the results compared to existing data? - Calibration and Validation
The objective of the validation work is to ensure that the product specifications are met long-term and in a full range of retrieval conditions, and to identify unfavorable conditions where performance of retrievals degrades. The initial assessment of SST accuracy will be performed against global reference SST fields (daily Reynolds OISST, OSTIA, etc.,) that are computed from numerous satellite data. Final SST validation will be performed with quality-controlled in situ SSTs. Along with SST product evaluation, the validation activity will include monitoring of clear-sky Brightness Temperatures (BT) associated with SSTs.
A more technical validation presentation, (PDF, 22.03 MB) is also available.