A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.
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2 Pith papers cite this work, alongside 262 external citations. Polarity classification is still indexing.
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CycloneMAE uses a TC structure-aware masked autoencoder with discrete probabilistic gridding and pre-train/fine-tune to deliver both deterministic and probabilistic forecasts, outperforming NWP systems in pressure and wind up to 120 hours and track up to 24 hours across five basins.
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StormNet: Improving storm surge predictions with a GNN-based spatio-temporal offset forecasting model
A spatio-temporal GNN model reduces storm surge water-level forecast RMSE by more than 70% for 48-hour horizons and over 50% for 72-hour horizons on U.S. Gulf Coast hurricane data.
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CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
CycloneMAE uses a TC structure-aware masked autoencoder with discrete probabilistic gridding and pre-train/fine-tune to deliver both deterministic and probabilistic forecasts, outperforming NWP systems in pressure and wind up to 120 hours and track up to 24 hours across five basins.