About time! Daily weather forecasts have been inaccurate beyond 24-48 hours for at least the past 6 decades.
Then we have these climate change demagogues asserting their climate models could forecast up to the year 2100. Absolutely ridiculous! Charlatans!
"DeepMind, Google’s AI laboratory, has developed a weather forecasting model that is both faster and more accurate than the European Centre for Medium-Range Weather Forecasts’ Ensemble Prediction System, a current leader in weather prediction. According to a recent paper in Nature, the DeepMind model demonstrated superior accuracy in 97.2 percent of cases and can generate forecasts in minutes rather than the hours required by conventional systems."
"... GenCast is a diffusion model, the type of generative AI model that underpins the recent, rapid advances in image, video and music generation. However, GenCast differs from these, in that it’s adapted to the spherical geometry of the Earth, and learns to accurately generate the complex probability distribution of future weather scenarios when given the most recent state of the weather as input.
To train GenCast, we provided it with four decades of historical weather data from ECMWF’s ERA5 archive. This data includes variables such as temperature, wind speed, and pressure at various altitudes. The model learned global weather patterns, at 0.25° resolution, directly from this processed weather data. ..."
From the abstract:
"Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP), which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts. GenCast is an ML weather prediction method, trained on decades of reanalysis data. 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. 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. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently."
GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy (original news release) "New AI model advances the prediction of weather uncertainties and risks, delivering faster, more accurate forecasts up to 15 days ahead"
Probabilistic weather forecasting with machine learning (open access)
Fig. 1: Schematic of how GenCast produces a forecast.
Fig. 2: Visualization of forecasts and tropical cyclone tracks.
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