THE GLOBAL ALL-WEATHER SEA SURFACE TEMPERATURE (GAWSST) ALGORITHM: A ROBUST METHOD FOR RAIN-CONTAMINATED REGIONS

Z. Jelenak1,2, S. Alsweiss³, C. Shoup³, P. Chang²

¹UCAR, ²NOAA/NESDIS/STAR, ³GTSC

This paper introduces the Global All-Weather Sea Surface Temperature (GAWSST) retrieval algorithm, a novel approach for accurately retrieving sea surface temperature (SST) from passive microwave radiometers in the presence of precipitation. Conventional operational SST retrieval methods, such as those applied to Advanced Microwave Scanning Radiometer-2 (AMSR-2) data, are highly susceptible to rain contamination, resulting in significant data gaps in critical areas. The GAWSST algorithm addresses this by employing a statistical approach based on a linear combination of vertically and horizontally polarized brightness temperatures (Tbs) from the 6, 10, and 18 GHz AMSR-2 channels. This methodology effectively minimizes atmospheric interference. The resultant linear combination of Tbs is a function of sea surface temperature, surface wind speed, and direction. Hence to retrieve the SST, the effects of wind speed and direction must be removed. To achieve this, an empirical relationship between the linear combination and these surface parameters is established. The algorithm is particularly powerful for observing SST within tropical cyclones, providing data to study immediate cooling effects. Validation against conventional methods, model outputs, and in-situ data from ocean assets deployed during the 2023-2025 hurricane seasons demonstrates its effectiveness. The results show that GAWSST provides more comprehensive coverage and captures detailed oceanic features with high fidelity, even in heavy precipitation. This work highlights GAWSST’s potential to significantly improve SST observations for weather forecasting and climate monitoring, with future work focusing on its application to other microwave radiometers such as Weather System Follow-on-Microwave (WSF-M).