Preliminary Test of Data Assimilation System with a Regional High-Resolution Atmosphere-Ocean Coupled Model Based on an Ensemble Kalman Filter

An ensemble Kalman filter (EnKF) using a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimate and to represent the uncertainties of SST in ensemble data assimilation strategies. The system was evaluated through data assimilation cycle experiments over a one-month period from July to August 2014, during which a tropical cyclone (TC) as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model reproduced SST distributions realistically even without assimilating SST and sea surface salinity observations, and therefore, atmospheric variables provided to ocean models can control oceanic variables physically to some extent. Investigations on the forecast error covariance revealed that the EnKF with the coupled model possibly lead to less flow-dependent error covariance for atmospheric variables near the surface owing to the difference of the variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. A verification for the analyses showed positive impacts of the application of the ocean model to EnKF on precipitation forecasts. The use of EnKF with the coupled model reproduced the intensity change of a TC realistically during the mature phase better than with an uncoupled atmosphere model.