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Keyword: tropical cyclone tracks ×Clear all

4 claims from 2 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Probabilistic weather forecasting with machine learning

   Price, Sánchez‐González, Alet et al. · Nature · 2024

   The paper introduces GenCast, a machine-learning probabilistic weather model that it reports has greater skill and speed than ENS, the European Centre for Medium-Range Weather Forecasts' ensemble forecast.

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   1. UncheckedGenCast, a machine-learning model, produces an ensemble of 15-day global forecasts at 0.25° resolution for over 80 variables in 8 minutes.“GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min.”
   2. UncheckedThe paper says GenCast, a machine-learning model, beat the leading ensemble forecast, ENS, on 97.2% of 1,320 evaluated targets and on several specific forecast types.“It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production.”

2. Earth and Planetary Sciences › Tropical and Extratropical Cyclones Research

   Advancing seasonal prediction of tropical cyclone activity with a hybrid AI-physics climate model

   Zhang, Rao, Yuval and Zhao · Environmental Research Letters · 2025

   The authors used Google's NeuralGCM hybrid AI-physics model to make July to November seasonal predictions of Northern Hemisphere tropical cyclone activity, and report useful skill despite simplified boundary conditions.

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   1. Unchecked“With such forcings, NeuralGCM can generate 100 simulation days in ~8 minutes with a single Graphics Processing Unit (GPU), while simulating realistic atmospheric circulation and TC climatology patterns.”
   2. UncheckedIn a hybrid AI-physics model, predicted and observed hurricane counts in the North Atlantic and East Pacific correlated at about 0.7 over 1990 to 2023.“Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990 to 2023 (r=~0.7), suggesting prediction skill comparable to existing physical GCMs.”