Estimating Model Error Covariances with Artificial Neural Networks

TL;DR

This paper explores using artificial neural networks to estimate and sample model error distributions, aiming to improve the construction of model error covariance matrices in data assimilation systems.

Contribution

It introduces a novel approach employing ANNs to sample model error distributions for better covariance matrix estimation in weak constraint 4D-Var.

Findings

ANN-based sampling improves model error covariance estimation

Application in ECMWF data assimilation enhances analysis accuracy

Demonstrates potential for further system improvements

Abstract

Methods to deal with systematic model errors are an increasingly important component of modern data assimilation systems and their effectiveness has increased in recent years thanks to advances in methodology and the quality and density of the global observing system. The weak constraint 4D-Var assimilation algorithm employed at ECMWF is well suited to the estimation and correction of model errors as they are explicitly accounted for in the cost function. This has led to significant improvements in recent years to the accuracy of stratospheric analyses. One question that remains open is about the estimation of the model error covariance matrix to use in weak constraint 4D-Var. Encouraged by the promising results we have obtained in the recent past through the use of Artificial Neural Networks (ANNs) to estimate slowly-varying model errors in the ECMWF assimilation cycle, we explore in this work the use of ANNs to sample the model error distribution and provide an alternative way to construct a model error covariance matrix. Results from the application of the new model error covariance in cycling assimilation experiments are described and implications for further developments of the ECMWF data assimilation system are discussed.