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Statistical Postprocessing Yields Accurate Probabilistic Forecasts from Artificial Intelligence Weather Models
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DOI：10.1175/AIES-D-25-0037.1.png)
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Abstract
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Artificial intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional numerical weather prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical postprocessing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against postprocessed outputs from the ECMWF high-resolution (HRES) and ensemble (ENS) models. Without any modification to processing workflows, postprocessing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical postprocessing methods developed for NWP are directly applicable to AI models, enabling national meteorological centers to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion.
Keywords:
Operational forecasting
Artificial intelligence
Machine learning
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