A Study of Coupling Parameter Estimation Implemented by 4D-Var and EnKF with a Simple Coupled System

作者：Han, Guijun; Wu, Xinrong^*; Zhang, Shaoqing; Liu, Zhengyu; Navon, Ionel Michael; Li, Wei

来源：Advances in Meteorology, 2015, 2015: 530764.

DOI：10.1155/2015/530764

• ENSEMBLE KALMAN FILTER
• ADAPTIVE COVARIANCE INFLATION
• VARIATIONAL DATA ASSIMILATION
• SEQUENTIAL DATA ASSIMILATION
• MEMORY BUNDLE METHOD
• OPERATIONAL IMPLEMENTATION
• CLIMATE ESTIMATION
• ADJUSTMENT
• ATMOSPHERE
• IMPACT

Coupling parameter estimation (CPE) that uses observations to estimate the parameters in a coupled model through error covariance between variables residing in different media may increase the consistency of estimated parameters in an air-sea coupled system. However, it is very challenging to accurately evaluate the error covariance between such variables due to the different characteristic time scales at which flows vary in different media. With a simple Lorenz-atmosphere and slab ocean coupled system that characterizes the interaction of two-timescale media in a coupled "climate" system, this study explores feasibility of the CPE with four-dimensional variational analysis and ensemble Kalman filter within a perfect observing system simulation experiment framework. It is found that both algorithms can improve the representation of air-sea coupling processes through CPE compared to state estimation only. These simple model studies provide some insights when parameter estimation is implemented with a coupled general circulation model for improving climate estimation and prediction initialization.

• 出版日期2015
• 单位北京大学; 国家海洋信息中心