Estimating forecast model bias in coupled global and limited-area models

Abstract

Idealised perfect model experiments suggest that performing data assimilation on a ‘composite’ state vector, constructed from global and limited-area model states, can be beneficial to both model states. Here, an illustrative scheme is implemented to account for systematic forecast errors attributed to the imperfect model dynamics. Results from numerical experiments suggest that even simple bias correction schemes can correct forecast errors in composite states.

Keywords

• ensemble Kalman Filter
• limited-area models
• composite state
• bias correction