Reproduction of Hydrothermodynamic Characteristics of the Western Arctic Seas of Russia with Assimilation of Sea Surface Temperature and Sea Ice Concentration Data

Reliable forecasting of meteorological, hydrothermodynamic, and ice characteristics in the waters of the Western Arctic Seas of Russia using atmospheric circulation, marine circulation, and sea ice models is currently impossible without observational data assimilation. Data assimilation improves the quality of the initial state of hydrophysical and ice characteristics in models for forecasting simulations, thereby enhancing their accuracy. This study presents a technique for assimilating satellite data on sea surface temperature (SST) and sea ice concentration (SIC) into the INMOM marine circulation model using the Data Assimilation Research Testbed (DART) software, with an assessment of the validity of the employed assimilation algorithm. A comparative analysis of the accuracy of hydrothermodynamic state forecasts with and without satellite SST and SIC data assimilation has been conducted. It is shown that assimilating satellite data reduce the root-mean-square deviation (RMSD) of 24-h forecast results from observational data by approximately 80 $$\%$$ for SST and by 60–70 $$\%$$ for SIC compared to the simulation without assimilation. The temporal variability of RMSD in SST and SIC forecasts indicates that the largest errors occur during periods of intense upper sea layer heating and ice melting. The importance of simultaneous assimilation of SST and SIC data is highlighted: more accurate SST reproduction improves the accuracy of heat and salt flux calculations at the ocean-ice boundary, which regulate the thermal accretion/melting processes of ice, consequently enhancing the reproduction of ice area and its edge. In turn, a more accurate SIC calculation directly improves the accuracy of heat flux calculations at the water–air boundary and, consequently, SST.