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

Key points

• Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models.1
• 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.1
• 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).1
• These results demonstrate broad improvements in ten-day weather forecasts through a short correction-training schedule while retaining the pretrained backbone.1

Sources (1)

• [1]GraphCast-Mamba: Learning Non-Markovian Corrections for Weather Forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:52 PM

  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.

  Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models.

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