Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911262792744960 |
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| author | Trotta, Belinda Johnson, Robert de Burgh-Day, Catherine Hudson, Debra Abellan, Esteban Canvin, James Kelly, Andrew Mentiplay, Daniel Owen, Benjamin Whelan, Jennifer |
| author_facet | Trotta, Belinda Johnson, Robert de Burgh-Day, Catherine Hudson, Debra Abellan, Esteban Canvin, James Kelly, Andrew Mentiplay, Daniel Owen, Benjamin Whelan, Jennifer |
| contents | 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 post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing 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 post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models Trotta, Belinda Johnson, Robert de Burgh-Day, Catherine Hudson, Debra Abellan, Esteban Canvin, James Kelly, Andrew Mentiplay, Daniel Owen, Benjamin Whelan, Jennifer Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning 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 post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing 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 post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion. |
| title | Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.12672 |