Atmosphere and sea surface temperature coupled data assimilation with enhanced use of SST-sensitive microwave radiances

Atmospheric state analysis that leverages state-of-the-art data assimilation achieves high accuracy and can provide initial conditions for numerical weather prediction (NWP) and climatological reanalysis. However, the interactions between the atmosphere and the ocean have been inadequately addressed, with sea surface temperature (SST) as a boundary condition for the atmosphere. This limitation impacts the accuracy of atmospheric state analyses and the utilization of SST-sensitive observations. To address this, we developed a partially coupled data assimilation (PCDA) system for the atmosphere and SST by extending the operational global NWP system of the Japan Meteorological Agency. The PCDA system enhances the analysis variables and background error covariance matrices to include SST components and the use of microwave radiance observations sensitive to SST, particularly at low frequencies (6–11 GHz), which have previously been unused or absent in most NWP systems. Our numerical experiments demonstrate several key findings: 1) the PCDA system identified colder SSTs in regions with significant SST gradients, including SST fronts in the midlatitudes, and we obtained zonally positive and negative increments in tropical instability wave regions; 2) the SST analysis produced by PCDA was consistent with independent SST analyses; 3) the system yielded a moist and warm low-level troposphere, leading to an increase in the first 24-h rain forecast near the intertropical convergence zone; and 4) PCDA globally improved the forecast accuracy of near-surface temperatures, with notable improvements in the tropics for most variables, except for midtropospheric temperature. In the extratropics, forecast accuracy improvements were observed for height and humidity although some degradation occurred mainly in the Southern Hemisphere. Significance Statement The objective of this study is to develop partially coupled data assimilation for the atmosphere and sea surface temperature by extending an operational global numerical weather prediction system. This is important because the current data assimilation does not fully consider interactions between the atmosphere and sea surface temperature, limiting the analysis and forecast accuracy of these systems. Our results show that enhancing the assimilation of microwave radiances from satellites, including low frequencies below 11 GHz, using the partially coupled data assimilation improves the analysis and forecast accuracy of these systems. These results point the way to a better analysis and understanding of the atmosphere–ocean coupled system.