Development of a Convection-Permitting Air–Sea Coupled Ensemble Data Assimilation System for Tropical Cyclone Prediction

A regional-scale fully coupled data assimilation (DA) system based on the ensemble Kalman filter (EnKF) is developed for a high-resolution coupled atmosphere-ocean model. Through the flow-dependent covariance both within and across the oceanic and atmospheric domains, the coupled DA system is capable of updating both atmospheric and oceanic state variables simultaneously by assimilating either atmospheric and/or oceanic observations. The potential impacts of oceanic observations, including sea-surface temperature, sea-surface height anomaly, and sea-surface current, in addition to the synthetic Hurricane Position and Intensity (HPI) observation, on tropical cyclone (TC) analysis and prediction are examined through observing system simulation experiments (OSSEs) of Hurricane Florence (2018). Results show that assimilation of oceanic observations not only resulted in a better analysis of the oceanic variables, but also considerably reduced analysis errors in the atmospheric fields, including the intensity and structure of Florence. Also improved are the prediction of tropical cyclone track and intensity initialized with the EnKF analysis mean assimilating oceanic observations. Results show promise in potential further improvement in TC prediction through assimilation of both atmospheric and oceanic observations using the ensemble-based fully coupled DA system.