Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918141398876160 |
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| author | Sha, Yingkai Schreck, John S. Chapman, William Gagne II, David John |
| author_facet | Sha, Yingkai Schreck, John S. Chapman, William Gagne II, David John |
| contents | Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts. This study presents a novel solution to tackle this problem -- integrating terrain-following coordinates into AIWP models. Forecast experiments are conducted to evaluate the effectiveness of terrain-following coordinates using FuXi, an example AIWP model, adapted to 1.0 degree grid spacing data. Verification results show a largely improved estimation of extreme events and precipitation intensity spectra. Terrain-following coordinates are also found to collaborate well with global mass and energy conservation constraints, with a clear reduction of drizzle bias. Case studies reveal that terrain-following coordinates can represent near-surface winds better, which helps AIWP models in learning the relationships between precipitation and other prognostic variables. The result of this study suggests that terrain-following coordinates are worth considering for AIWP models in producing more accurate precipitation forecasts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00332 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts Sha, Yingkai Schreck, John S. Chapman, William Gagne II, David John Atmospheric and Oceanic Physics Artificial Intelligence Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts. This study presents a novel solution to tackle this problem -- integrating terrain-following coordinates into AIWP models. Forecast experiments are conducted to evaluate the effectiveness of terrain-following coordinates using FuXi, an example AIWP model, adapted to 1.0 degree grid spacing data. Verification results show a largely improved estimation of extreme events and precipitation intensity spectra. Terrain-following coordinates are also found to collaborate well with global mass and energy conservation constraints, with a clear reduction of drizzle bias. Case studies reveal that terrain-following coordinates can represent near-surface winds better, which helps AIWP models in learning the relationships between precipitation and other prognostic variables. The result of this study suggests that terrain-following coordinates are worth considering for AIWP models in producing more accurate precipitation forecasts. |
| title | Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2503.00332 |