ArchesWeather: An efficient AI weather forecasting model at 15\deg resolution
<|reference_start|>ArchesWeather: An efficient AI weather forecasting model at 15\deg resolution: 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{\deg} 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{\deg} 50-members NeuralGCM ensemble for one to three days ahead forecasting. Our code and models are publicly available at https://github.com/gcouairon/ArchesWeather.<|reference_end|>
@article{couairon2024archesweather:,
    title={ArchesWeather: An efficient AI weather forecasting model at 1.5{\deg}
  resolution},
    author={Guillaume Couairon, Christian Lessig, Anastase Charantonis, Claire
  Monteleoni},
    journal={arXiv preprint arXiv:2405.14527},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14527},
    primaryClass={cs.LG cs.AI}
}