Exploring the structure of time-correlated model errors in the ECMWF Data Assimilation System

TL;DR

This paper investigates the temporal structure of systematic model errors in the ECMWF data assimilation system, assessing current correction methods and proposing new strategies for improved error estimation and correction.

Contribution

It introduces the use of LAIG diagnostics to analyze time-correlated model errors and evaluates the weak constraint 4DVar algorithm's effectiveness in error reduction.

Findings

LAIG diagnostics reveal significant time correlation in model errors.

Weak constraint 4DVar partially reduces systematic errors.

New ideas for model error correction are proposed based on findings.

Abstract

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 (LAIG) 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 4DVar 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.