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

Guijun Han,Zhengyu Liu,Wei Li,Ionel Michael Navon,Xinrong Wu,Shaoqing Zhang

doi:10.1155/2015/530764

Abstract

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.

Highlights

Due to its potential to reduce initial shocks between different media in a coupled climate system, coupled data assimilation (CDA) that uses coupled model dynamics to extract observational information in one or more media is emerging as an important topic in the climate community ([1,2,3,4,5,6] the related discussion at the Sixth World Meteorological Organization Data Assimilation Symposium)
A simple coupled model that characterizes the interaction of media with two different time scales is used to study the feasibility of the 4D-Var and ensemble Kalman filter (EnKF) coupling parameter estimation (CPE)
Within a perfect observing system simulation experiment (OSSE) framework which assumes that model errors only arise from the erroneously-set coupling parameters, the results demonstrate that, compared to traditional state estimation, both 4D-Var CPE and EnKF CPE algorithms can greatly improve the representation of air-sea coupling processes

Summary

Introduction

Due to its potential to reduce initial shocks between different media in a coupled climate system, coupled data assimilation (CDA) that uses coupled model dynamics to extract observational information in one or more media is emerging as an important topic in the climate community ([1,2,3,4,5,6] the related discussion at the Sixth World Meteorological Organization Data Assimilation Symposium (http://das6.cscamm.umd .edu/)). Data assimilation in a multiple space and time scale system has been explored [2, 28], the CPE in such a system has not been fully investigated Both 4D-Var and EnKF can be used to implement CPE. It is well known that they originated from the same information estimation theory (Bayes’ rule), different numerical implementations make different performances of 4D-Var and EnKF data assimilation [17]. EnKF CPE uses flowdependent coupling error covariance (i.e., error covariance between a model variable in the observational medium and a parameter in another medium) to project observational information onto the parameter being estimated, thereby implementing CPE in a sequential manner

Methods

Results

Conclusion