Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910310093291520 |
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| author | Park, Young-Jae Seo, Minseok Kim, Doyi Kim, Hyeri Choi, Sanghoon Choi, Beomkyu Ryu, Jeongwon Son, Sohee Jeon, Hae-Gon Choi, Yeji |
| author_facet | Park, Young-Jae Seo, Minseok Kim, Doyi Kim, Hyeri Choi, Sanghoon Choi, Beomkyu Ryu, Jeongwon Son, Sohee Jeon, Hae-Gon Choi, Yeji |
| contents | In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction is crucial for effective damage control. Traditional physics-based models, while comprehensive, are computationally intensive and rely heavily on the expertise of forecasters. Contemporary data-driven methods often rely on reanalysis data, which can be considered to be the closest to the true representation of weather conditions. However, reanalysis data is not produced in real-time and requires time for adjustment because prediction models are calibrated with observational data. This reanalysis data, such as ERA5, falls short in challenging real-world situations. Optimal preparedness necessitates predictions at least 72 hours in advance, beyond the capabilities of standard physics models. In response to these constraints, we present an approach that harnesses real-time Unified Model (UM) data, sidestepping the limitations of reanalysis data. Our model provides predictions at 6-hour intervals for up to 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models. In line with our efforts to mitigate adversities inflicted by \rthree{typhoons}, we release our preprocessed \textit{PHYSICS TRACK} dataset, which includes ERA5 reanalysis data, typhoon best-track, and UM forecast data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_15726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data Park, Young-Jae Seo, Minseok Kim, Doyi Kim, Hyeri Choi, Sanghoon Choi, Beomkyu Ryu, Jeongwon Son, Sohee Jeon, Hae-Gon Choi, Yeji Computer Vision and Pattern Recognition In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction is crucial for effective damage control. Traditional physics-based models, while comprehensive, are computationally intensive and rely heavily on the expertise of forecasters. Contemporary data-driven methods often rely on reanalysis data, which can be considered to be the closest to the true representation of weather conditions. However, reanalysis data is not produced in real-time and requires time for adjustment because prediction models are calibrated with observational data. This reanalysis data, such as ERA5, falls short in challenging real-world situations. Optimal preparedness necessitates predictions at least 72 hours in advance, beyond the capabilities of standard physics models. In response to these constraints, we present an approach that harnesses real-time Unified Model (UM) data, sidestepping the limitations of reanalysis data. Our model provides predictions at 6-hour intervals for up to 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models. In line with our efforts to mitigate adversities inflicted by \rthree{typhoons}, we release our preprocessed \textit{PHYSICS TRACK} dataset, which includes ERA5 reanalysis data, typhoon best-track, and UM forecast data. |
| title | Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.15726 |