ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution
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| Format: | Preprint |
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2024
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| _version_ | 1866913415431192576 |
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| author | Couairon, Guillaume Lessig, Christian Charantonis, Anastase Monteleoni, Claire |
| author_facet | Couairon, Guillaume Lessig, Christian Charantonis, Anastase Monteleoni, Claire |
| contents | One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local neural interactions, like 3D convolutions or 3D local attention windows as in Pangu-Weather. On the other hand, some works have shown great success in weather forecasting without this locality principle, at the cost of a much higher parameter count. In this paper, we show that the 3D local processing in Pangu-Weather is computationally sub-optimal. We design ArchesWeather, a transformer model that combines 2D attention with a column-wise attention-based feature interaction module, and demonstrate that this design improves forecasting skill.
ArchesWeather is trained at 1.5° resolution and 24h lead time, with a training budget of a few GPU-days and a lower inference cost than competing methods. An ensemble of four of our models shows better RMSE scores than the IFS HRES and is competitive with the 1.4° 50-members NeuralGCM ensemble for one to three days ahead forecasting. Our code and models are publicly available at https://github.com/gcouairon/ArchesWeather. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_14527 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution Couairon, Guillaume Lessig, Christian Charantonis, Anastase Monteleoni, Claire Machine Learning Artificial Intelligence One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local neural interactions, like 3D convolutions or 3D local attention windows as in Pangu-Weather. On the other hand, some works have shown great success in weather forecasting without this locality principle, at the cost of a much higher parameter count. In this paper, we show that the 3D local processing in Pangu-Weather is computationally sub-optimal. We design ArchesWeather, a transformer model that combines 2D attention with a column-wise attention-based feature interaction module, and demonstrate that this design improves forecasting skill. ArchesWeather is trained at 1.5° resolution and 24h lead time, with a training budget of a few GPU-days and a lower inference cost than competing methods. An ensemble of four of our models shows better RMSE scores than the IFS HRES and is competitive with the 1.4° 50-members NeuralGCM ensemble for one to three days ahead forecasting. Our code and models are publicly available at https://github.com/gcouairon/ArchesWeather. |
| title | ArchesWeather: An efficient AI weather forecasting model at 1.5° resolution |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.14527 |