Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1

TL;DR

This paper introduces the first successful use of ensemble data assimilation with AI-based weather prediction models, demonstrating its stability and potential for diagnosing model properties and error growth in ClimaX v0.3.1.

Contribution

It pioneers the integration of ensemble Kalman filter with AI weather models, enabling model diagnosis and evaluation of error covariance in sparse observation regions.

Findings

Ensemble data assimilation was stably implemented with ClimaX.

AI forecasts showed reasonable error covariance in sparse regions.

Error growth in AI models was weaker than in dynamical models.

Abstract

Artificial intelligence (AI)-based weather prediction research is growing rapidly and has shown to be competitive with the advanced dynamic numerical weather prediction models. However, research combining AI-based weather prediction models with data assimilation remains limited partially because long-term sequential data assimilation cycles are required to evaluate data assimilation systems. This study proposes using ensemble data assimilation for diagnosing AI-based weather prediction models, and marked the first successful implementation of ensemble Kalman filter with AI-based weather prediction models. Our experiments with an AI-based model ClimaX demonstrated that the ensemble data assimilation cycled stably for the AI-based weather prediction model using covariance inflation and localization techniques within the ensemble Kalman filter. While ClimaX showed some limitations in capturing flow-dependent error covariance compared to dynamical models, the AI-based ensemble forecasts provided reasonable and beneficial error covariance in sparsely observed regions. In addition, ensemble data assimilation revealed that error growth based on ensemble ClimaX predictions was weaker than that of dynamical NWP models, leading to higher inflation factors. A series of experiments demonstrated that ensemble data assimilation can be used to diagnose properties of AI weather prediction models such as physical consistency and accurate error growth representation.