Impact of assimilating satellite and glider observations on Hurricane Isaias (2020) forecast using marine JEDI Weather and Forecasting

Realistic ocean initial conditions are essential for coupled hurricane forecasts. This study focuses on the impact of assimilating high-resolution ocean observations for initialization of the Modular Ocean Model (MOM6) in a coupled configuration with the Hurricane Analysis and Forecast System (HAFS). Based on the Joint Effort for Data Assimilation Integration (JEDI) framework, numerical experiments were performed for the Hurricane Isaias (2020) case, a Category One hurricane, with use of underwater glider data sets and satellite observations. Assimilation of ocean glider data together with satellite observations provides opportunity to further advance understanding of ocean conditions and air-sea interactions in coupled model initialization and Hurricane forecast systems. This comprehensive data assimilation approach has led to a more accurate prediction of the salinity-induced barrier layer thickness that suppresses vertical mixing and sea surface temperature cooling during the storm. Increased barrier layer thickness enhances ocean enthalpy flux into the lower atmosphere and potentially increases tropical cyclone intensity. Assimilation of satellite observations demonstrates improvement in Hurricane Isaias’ intensity forecast. Assimilating glider observations with broad spatial and temporal coverage along Isaias’ track in addition to satellite observations further increase Isaias’ intensity forecast. Overall this case study demonstrates the importance of assimilating comprehensive marine observations to a more robust ocean and hurricane forecast under a unified JEDI-HAFS hurricane forecast system.