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Relaxation experiments in ML-based weather prediction models to study subseasonal predictability

Year: 2025

Type: preprint

Abstract: This study explores the use of relaxation experiments in machine learning-based weather prediction (MLWP) models to identify sources of subseasonal forecast errors and compares their effectiveness to a traditional numerical weather prediction (NWP) system. Relaxation involves nudging specific regions of a model toward reanalysis data to isolate their influence on forecast skill. We apply this technique to two MLWP models, Pangu-Weather (fully data-driven) and NeuralGCM (hybrid) and compare them to the Unified Forecast System (UFS) during Week 3–4 forecasts of two major precipitation events in western North America in winter 2022/2023, both linked to Madden-Julian Oscillation (MJO) activity. MLWP models outperform UFS at subseasonal lead times. While tropical relaxation improves all forecasts, gains are greater for UFS, reflecting MLWP models’ stronger baseline performance. These results highlight relaxation experiment as a low-cost, effective diagnostic for understanding and improving subseasonal forecasts, especially in emerging MLWP systems.

DOI: 10.22541/essoar.175883412.26972897/v1

Authors: Siyu Li, Juliana Dias, Benjamin John Moore, Julian F. Quinting

Institutions: Karlsruhe Institute of Technology, NOAA Physical Sciences Laboratory, Scripps Institution of Oceanography

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: gold

Funders: European Commission, National Oceanic and Atmospheric Administration, HORIZON EUROPE European Research Council

Award: Advancing Subseasonal PredIctions at Reduced computational Effort

OpenAlex ID: W4414495128

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