A New High-Resolution Sea Surface Temperature Blended Analysis

Eileen Maturi, NOAA/NESDIS, College Park, MD, United States, Andrew Harris, NOAA/NESDIS/STAR; University of Maryland, CICS, College Park, MD, United States, Gary A Wick, NOAA/ESRL, Boulder, CO, United States, John Sapper, NOAA OSPO, College Park, MD, United States and Maureen Madden, DOC/NOAA/NESDIS, College Park, MD, United States
Abstract:
The National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite Data and Services (NESDIS) is now generating daily 5-km global high resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines geostationary (GOES-E/W, MT-SAT and Meteosat) SST and S-NPP VIIRS and MetOp-B (AVHRR) SST data into a single high-resolution (0.05°×0.05°) product. An earlier version was produced at 0.1°×0.1°, a resolution chosen to approximate the Nyquist sampling criterion for the mid-latitude Rossby radius (~20 km) in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher resolution grid-spacing is more successful in this regard. The analysis employs a rigorous multi-scale optimal interpolation methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale to ensure a good balance between detail preservation and noise reduction. The product accuracy verified against globally distributed buoys is ~0.02 K, with a robust standard deviation of ~0.25 K. The new analysis has proven a significant success, even when compared to other products that purport to be of similar resolution, and is being used as the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction. Forthcoming enhancements include the incorporation of Microwave SST products from the GCOM-W1 AMSR-2 instrument to improve resolution of SST features in areas of persistent cloud, and explicit correction for diurnal effects via a turbulence model of upper ocean heating.