Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909532077162496 |
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| author | Feldmann, Monika Beucler, Tom Gomez, Milton Martius, Olivia |
| author_facet | Feldmann, Monika Beucler, Tom Gomez, Milton Martius, Olivia |
| contents | Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models Feldmann, Monika Beucler, Tom Gomez, Milton Martius, Olivia Atmospheric and Oceanic Physics Machine Learning Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models towards process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of three top-performing AI-models for convective parameters at lead-times of up to 10 days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments. |
| title | Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2406.09474 |