On the sensitivity of different ensemble filters to the type of assimilated observation networks

TL;DR

This paper compares an AI-based ensemble filter with the standard LETKF in a surface quasi-geostrophic model, examining how observation network characteristics influence data assimilation performance and error characteristics.

Contribution

It introduces a comparison between a new AI-based ensemble filter and the standard LETKF, highlighting their different sensitivities to observation network properties.

Findings

Analysis solutions vary with the number and distribution of observations.

Significant changes observed in multiscale error characteristics.

Results suggest the importance of observation network design for advanced DA methods.

Abstract

Recent advances in data assimilation (DA) have focused on developing more flexible approaches that can better accommodate nonlinearities in models and observations. However, it remains unclear how the performance of these advanced methods depends on the observation network characteristics. In this study, we present initial experiments with the surface quasi-geostrophic model, in which we compare a recently developed AI-based ensemble filter with the standard Local Ensemble Transform Kalman Filter (LETKF). Our results show that the analysis solutions respond differently to the number, spatial distribution, and nonlinear fraction of assimilated observations. We also find notable changes in the multiscale characteristics of the analysis errors. Given that standard DA techniques will be eventually replaced by more advanced methods, we hope this study sets the ground for future efforts to reassess the value of Earth observation systems in the context of newly emerging algorithms.