Bridging Artificial Intelligence and Data Assimilation: The Data-driven Ensemble Forecasting System ClimaX-LETKF

TL;DR

This paper introduces ClimaX-LETKF, a novel data-driven ensemble weather forecasting system that integrates observations with machine learning, demonstrating improved stability and accuracy over traditional NWP models.

Contribution

It presents the first purely data-driven ML-based ensemble weather forecasting system that assimilates real observations independently of NWP models.

Findings

ClimaX-LETKF operates stably over multiple years.

RTPP improves accuracy and stability in MLWP.

ML models are less capable of restoring atmospheric attractors than NWP models.

Abstract

While machine learning-based weather prediction (MLWP) has achieved significant advancements, research on assimilating real observations or ensemble forecasts within MLWP models remains limited. We introduce ClimaX-LETKF, the first purely data-driven ML-based ensemble weather forecasting system. It operates stably over multiple years, independently of numerical weather prediction (NWP) models, by assimilating the NCEP ADP Global Upper Air and Surface Weather Observations. The system demonstrates greater stability and accuracy with relaxation to prior perturbation (RTPP) than with relaxation to prior spread (RTPS), while NWP models tend to be more stable with RTPS. RTPP replaces an analysis perturbation with a weighted blend of analysis and background perturbations, whereas RTPS simply rescales the analysis perturbation. Our experiments reveal that MLWP models are less capable of restoring the atmospheric field to its attractor than NWP models. This work provides valuable insights for enhancing MLWP ensemble forecasting systems and represents a substantial step toward their practical applications.