On the Localization in Strongly Coupled Ensemble Data Assimilation Using a Two‐Scale Lorenz Model

Earth and Space Science

卷:8

Zheqi Shen¹  Youmin Tang¹  Xiaojing Li²  Yanqiu Gao² 

[1]Key Laboratory of Marine Hazards Forecasting Ministry of Natural Resources Hohai University Nanjing China

[2]|State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of Oceanography State Oceanic Administration Hangzhou China

关键词: cross‐domain covariance;    data assimilation;    ensemble Kalman filter;    localization;    strongly coupled data assimilation;    two scale Lorenz model;   

DOI  :  10.1029/2020EA001465

来源: DOAJ

【 摘 要 】

Abstract For coupled numerical models with different components (domains), there are two kinds of assimilation strategies applied for producing ocean analysis and initial condition of predictions: the strongly coupled data assimilation (SCDA) and weakly coupled data assimilation (WCDA). The former needs to accurately estimate cross‐component error covariances, which is much challenging, especially when a small ensemble size's Kalman filter‐based algorithm is used and a coupled model has the components of different spatiotemporal scales. In this study, we propose a new scheme for the ensemble adjustment Kalman filter (EAKF) to address cross‐component localization, a critical issue in estimating the cross‐component error covariance in SCDA, based on a two‐scale Lorenz ’96 coupled mode with different temporal and spatial scales. Emphasis places on designing the cross‐component localization factors in the framework of multiple spatial scales. The result shows that the SCDA can provide much more accurate estimations of the states than the WCDA when the new proposed cross‐component localization is used. A further analysis reveals that the advantage of the SCDA over the WCDA is attributed to the assimilation of observations from the small‐scale model in the coupled system, whereas the contribution of the assimilation of observations from the large‐scale model is not obvious. This study offers a useful technique to develop SCDA system in operational prediction models, which is being pursued in the prediction community.

【 授权许可】

Unknown