An ensemble Kalman filter with a high-resolution atmosphere-ocean coupled model for tropical cyclone forecasts

Abstract:

An ensemble Kalman filter (EnKF) using a regional mesoscale atmosphere–ocean coupled model was developed to represent the uncertainties of sea surface temperature (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 as well as severe rainfall events occurred. The results showed that the data assimilation cycle with the coupled model could reproduce SST distributions realistically even without updating SST and salinity during the data assimilation cycle. Therefore, atmospheric variables and radiation applied as a forcing to ocean models can control oceanic variables to some extent in the current data assimilation configuration. However, investigations of the forecast error covariance estimated in EnKF revealed that the correlation between atmospheric and oceanic variables could possibly lead to less flow-dependent error covariance for atmospheric variables owing to the difference in the time scales between atmospheric and oceanic variables. A verification of the analyses showed positive impacts of applying the ocean model to EnKF on precipitation forecasts. The use of EnKF with the coupled model system captured intensity changes of a tropical cyclone better than it did with an uncoupled atmosphere model, even though the impact on the track forecast was negligibly small.