← 研踪 PaperTrailarXiv:2610.35545 · v1
Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attention
首次提交 2026-10-26 22:53
版本更新 2026-10-26 22:53
雷达观测 2026-10-29 00:35
摘要
High-resolution probabilistic weather forecasting remains computationally demanding. Here we present Astra, a 0.25$^\circ$ ensemble model trained on eight H200 GPUs. It combines Kantorovich Attention, a two-sided transport attention in which each region chooses where it reads from and writes to, so every region sends and receives at token resolution, with a self-featurised continuous ranked probability score (CRPS) that also scores forecasts in the model's multiscale features. In WeatherBench 2, Astra matches GenCast's CRPS averaged over five upper-air targets and days 1-10 with 9.8% of its hardware-normalised training compute. Verified against ERA5, its CRPS and ensemble-mean root-mean-square error are lower than the ECMWF ensemble's on all 600 variable, level and lead-time combinations, and its CRPS is lower than GenCast's on 73.5% of them. A 15-day member needs 39 times fewer floating-point operations than GenCast's GPU implementation, and 50 members take under 40 min on one consumer GPU, within 21 GB.
作者备注：32 pages, 6 figures, 7 Extended Data figures, 1 Extended Data table
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v1 · 2026-10-26 22:53 · 最新观测版本
Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attention
High-resolution probabilistic weather forecasting remains computationally demanding. Here we present Astra, a 0.25$^\circ$ ensemble model trained on eight H200 GPUs. It combines Kantorovich Attention, a two-sided transport attention in which each region chooses where it reads from and writes to, so every region sends and receives at token resolution, with a self-featurised continuous ranked probability score (CRPS) that also scores forecasts in the model's multiscale features. In WeatherBench 2, Astra matches GenCast's CRPS averaged over five upper-air targets and days 1-10 with 9.8% of its hardware-normalised training compute. Verified against ERA5, its CRPS and ensemble-mean root-mean-square error are lower than the ECMWF ensemble's on all 600 variable, level and lead-time combinations, and its CRPS is lower than GenCast's on 73.5% of them. A 15-day member needs 39 times fewer floating-point operations than GenCast's GPU implementation, and 50 members take under 40 min on one consumer GPU, within 21 GB.
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