Assimilation of SST Data into a Real-Time Coastal Ocean Forecast System for the U.S. East Coast

Abstract

The real-time, three-dimensional, limited-area Coastal Ocean Forecast System (COFS) has been developed for the northwestern Atlantic Ocean and implemented at the National Centers for Environmental Prediction. COFS generates a daily nowcast and 1-day forecast of water level, temperature, salinity, and currents. Surface forcing is provided by 3-h forecasts from the National Weather Service's Eta Model, a mesoscale atmospheric prediction model. Lateral oceanic boundary conditions are based on climatic data. COFS assimilates in situ sea surface temperature (SST) observations and multichannel satellite SST retrievals for the past 48 h. SST predictions from the assimilating and nonassimilating versions of COFS were compared with independent observations and a 14-km-resolution multichannel SST analysis. The assimilation of SST data reduced the magnitude and the geographic extent of COFS's characteristic positive temperature bias north of the Gulf Stream. The root-mean-square SST differences between the COFS predictions and in situ observations were reduced by up to 47%?50%. Qualitative comparisons were also made between predictions from the assimilating and nonassimilating versions and thermal profiles measured by expendable bathythermographs. These comparisons indicated that the assimilation scheme had positive impact in reducing temperature differences in the top 300 m at most locations. However, the subsurface comparisons also show that, in dynamically complex regions such as the Gulf Stream, the continental slope, or the Gulf of Maine, the data assimilation system has difficulty reproducing the observed ocean thermal structure and would likely benefit from the direct assimilation of observed profiles.