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GraphCast-Mamba: Learning Non-Markovian Corrections for Weather Forecasting

Ilya Vilkoviskiy, Liang-Hong Mo, Suozhi Huang, Dmitry A. Abanin · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28512 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

修正されていない大気のプロセスは、多くの天気予報モデルによって使用される短い入力ウィンドウを超える歴史依存を導入することができます。 GraphCast--Mambaを導入し、凍結された$1\circ$ GraphCastバックボンは、トレーニング可能なMamba修正部門を使用して、グラフメッセージの転送とMamba層を組み合わせた歴史依存を導入します。この部門は、6時間連続の予測ステップを通じて時間メモリ状態を保持し、GraphCastの2フレーム入力を超える時間の文脈によって知られる修正を学びます。トレーニングは、2015〜2021年のERA5データを使用して修正部門に限定されています。2500の最適化アップデート

Machine translation (Meta M2M100); the English original follows.

Abstract

Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models. We introduce GraphCast--Mamba, which augments a frozen, pretrained $1^\circ$ GraphCast backbone with a trainable Mamba correction branch combining graph message passing and Mamba layers. The branch retains a temporal memory state across successive six-hour forecast steps, learning corrections informed by temporal context beyond GraphCast's two-frame input. Training is confined to the correction branch, using ERA5 data from 2015--2021. After 2,500 optimizer updates, including warm-up, the selected model achieves substantial improvements relative to the uncorrected GraphCast backbone. It reduces the GraphCast loss (normalized weighted mean-squared error; MSE) by 14.42% when aggregated over the full ten-day forecast and by 21.66% at day 10 (240 h after forecast initialization). At this final forecast lead time, root-mean-square error (RMSE) decreases by 7.09--14.60% across all 11 evaluated variable groups, spanning temperature, wind, pressure, geopotential, specific humidity, and precipitation. These results demonstrate broad improvements in ten-day weather forecasts through a short correction-training schedule while retaining the pretrained backbone.

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Topics: Context (archaeology), Computer science, Schedule, Meteorology, Weather forecasting, Forecast skill

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