OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916541526704128 |
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| author | Zhao, Pengcheng Bian, Jiang Ni, Zekun Jin, Weixin Weyn, Jonathan Fang, Zuliang Xiang, Siqi Dong, Haiyu Zhang, Bin Sun, Hongyu Thambiratnam, Kit Zhang, Qi |
| author_facet | Zhao, Pengcheng Bian, Jiang Ni, Zekun Jin, Weixin Weyn, Jonathan Fang, Zuliang Xiang, Siqi Dong, Haiyu Zhang, Bin Sun, Hongyu Thambiratnam, Kit Zhang, Qi |
| contents | In recent years, Artificial Intelligence Weather Prediction (AIWP) models have achieved performance comparable to, or even surpassing, traditional Numerical Weather Prediction (NWP) models by leveraging reanalysis data. However, a less-explored approach involves training AIWP models directly on observational data, enhancing computational efficiency and improving forecast accuracy by reducing the uncertainties introduced through data assimilation processes. In this study, we propose OMG-HD, a novel AI-based regional high-resolution weather forecasting model designed to make predictions directly from observational data sources, including surface stations, radar, and satellite, thereby removing the need for operational data assimilation. Our evaluation shows that OMG-HD outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution operational forecasting system, IFS-HRES, and the High-Resolution Rapid Refresh (HRRR) model at lead times of up to 12 hours across the contiguous United States (CONUS) region. We achieve up to a 13% improvement on RMSE for 2-meter temperature, 17% on 10-meter wind speed, 48% on 2-meter specific humidity, and 32% on surface pressure compared to HRRR. Our method shows that it is possible to use AI-driven approaches for rapid weather predictions without relying on NWP-derived weather fields as model input. This is a promising step towards using observational data directly to make operational forecasts with AIWP models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_18239 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations Zhao, Pengcheng Bian, Jiang Ni, Zekun Jin, Weixin Weyn, Jonathan Fang, Zuliang Xiang, Siqi Dong, Haiyu Zhang, Bin Sun, Hongyu Thambiratnam, Kit Zhang, Qi Atmospheric and Oceanic Physics Machine Learning In recent years, Artificial Intelligence Weather Prediction (AIWP) models have achieved performance comparable to, or even surpassing, traditional Numerical Weather Prediction (NWP) models by leveraging reanalysis data. However, a less-explored approach involves training AIWP models directly on observational data, enhancing computational efficiency and improving forecast accuracy by reducing the uncertainties introduced through data assimilation processes. In this study, we propose OMG-HD, a novel AI-based regional high-resolution weather forecasting model designed to make predictions directly from observational data sources, including surface stations, radar, and satellite, thereby removing the need for operational data assimilation. Our evaluation shows that OMG-HD outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution operational forecasting system, IFS-HRES, and the High-Resolution Rapid Refresh (HRRR) model at lead times of up to 12 hours across the contiguous United States (CONUS) region. We achieve up to a 13% improvement on RMSE for 2-meter temperature, 17% on 10-meter wind speed, 48% on 2-meter specific humidity, and 32% on surface pressure compared to HRRR. Our method shows that it is possible to use AI-driven approaches for rapid weather predictions without relying on NWP-derived weather fields as model input. This is a promising step towards using observational data directly to make operational forecasts with AIWP models. |
| title | OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2412.18239 |