Predictability of Global AI Weather Models
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917794153496576 |
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| author | Kieu, Chanh |
| author_facet | Kieu, Chanh |
| contents | This study examines the predictability of artificial intelligence (AI) models for weather prediction. Using a simple deep-learning architecture based on convolutional long short-term memory and the ERA5 data for training, we show that different time-stepping techniques can have a strong influence on the model performance and weather predictability. Specifically, a small-step approach for which the future state is predicted by recursively iterating an AI model over a small time increment displays strong sensitivity to the type of input channels, the number of data frames, or forecast lead times. In contrast, a big-step approach for which a current state is directly projected to a future state at each corresponding lead time provides much better forecast skill and a longer predictability range. In particular, the big-step approach is very resilient to different input channels, or data frames. In this regard, our results present a different method for implementing global AI models for weather prediction, which can optimize the model performance even with minimum input channels or data frames. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Predictability of Global AI Weather Models Kieu, Chanh Atmospheric and Oceanic Physics Chaotic Dynamics This study examines the predictability of artificial intelligence (AI) models for weather prediction. Using a simple deep-learning architecture based on convolutional long short-term memory and the ERA5 data for training, we show that different time-stepping techniques can have a strong influence on the model performance and weather predictability. Specifically, a small-step approach for which the future state is predicted by recursively iterating an AI model over a small time increment displays strong sensitivity to the type of input channels, the number of data frames, or forecast lead times. In contrast, a big-step approach for which a current state is directly projected to a future state at each corresponding lead time provides much better forecast skill and a longer predictability range. In particular, the big-step approach is very resilient to different input channels, or data frames. In this regard, our results present a different method for implementing global AI models for weather prediction, which can optimize the model performance even with minimum input channels or data frames. |
| title | Predictability of Global AI Weather Models |
| topic | Atmospheric and Oceanic Physics Chaotic Dynamics |
| url | https://arxiv.org/abs/2410.03266 |