Exploring the structure of time-correlated model errors in the ECMWF data assimilation system,Quarterly Journal of the Royal Meteorological Society

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Exploring the structure of time-correlated model errors in the ECMWF data assimilation system
Quarterly Journal of the Royal Meteorological Society ( IF 3.0 ) Pub Date : 2021-07-22 , DOI: 10.1002/qj.4137
Massimo Bonavita ₁

Affiliation

Model errors are increasingly seen as a fundamental performance limiter in both Numerical Weather Prediction and Climate Prediction simulations run with state-of-the-art Earth-system digital twins. This has motivated recent efforts aimed at estimating and correcting the systematic, predictable components of model error in a consistent data assimilation framework. While encouraging results have been obtained with a careful examination of the spatial aspects of the model error estimates, less attention has been devoted to the time correlation aspects of model errors and their impact on the assimilation cycle. In this work we employ a Lagged Analysis Increment Covariance (LAIC) diagnostic to gain insight in the temporal evolution of systematic model errors in the ECMWF operational data assimilation system, evaluate the effectiveness of the current weak constraint 4D-Var algorithm in reducing these types of errors and, based on these findings, start exploring new ideas for the development of model error estimation and correction strategies in data assimilation.

中文翻译：

探索 ECMWF 资料同化系统中时间相关模型误差的结构

在使用最先进的地球系统数字孪生模型运行的数值天气预报和气候预测模拟中，模型误差越来越被视为基本性能限制因素。这激发了最近的努力，旨在在一致的数据同化框架中估计和纠正模型误差的系统性、可预测成分。虽然通过仔细检查模型误差估计的空间方面获得了令人鼓舞的结果，但较少关注模型误差的时间相关性方面及其对同化周期的影响。在这项工作中，我们采用滞后分析增量协方差 (LAIC) 诊断来深入了解 ECMWF 操作数据同化系统中系统模型误差的时间演变，

更新日期：2021-09-06

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