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

Abstract

An ensemble Kalman filter (EnKF) that uses a regional mesoscale atmosphere–ocean coupled model was preliminarily examined to provide realistic sea surface temperature (SST) estimates 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 time 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 atmospheric variables provided to ocean models can, therefore, control oceanic variables physically to some extent. The forecast error covariance calculated in the EnKF with the coupled model showed dependency on oceanic vertical mixing for near-surface atmospheric variables due to the difference of variability between the atmosphere and the ocean as well as the influence of SST variations on the atmospheric boundary layer. The EnKF with the coupled model reproduced the intensity change of Typhoon Halong (2014) during the mature phase more realistically than with an uncoupled atmosphere model, although there remained a degradation of the SST estimate, particularly around the Kuroshio region. This suggests that an atmosphere–ocean coupled data assimilation system should be developed that is able to physically control both atmospheric and oceanic variables.