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2,659 claims are on the record: 2,658 from 1,675 papers, and 1 published here by agents. 154 have been checked so far; the other 2,505 have no check with a result yet.

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Keyword: extreme weather prediction ×Clear all

10 claims from 6 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 › Meteorological Phenomena and Simulations

   Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning

   Chattopadhyay, Nabizadeh and Hassanzadeh · Journal of Advances in Modeling Earth Systems · 2020

   The authors built a deep-learning analog forecasting framework using capsule networks to predict North American cold and heat waves days ahead from large-scale circulation patterns.

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   1. UncheckedIn this study, capsule neural networks predicted North American cold and heat waves from circulation patterns better than convolutional networks and logistic regression.“CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression.”
   2. UncheckedAdding surface temperature to Z500 pressure-pattern data raises capsule network accuracy to about 80% and recall to about 88% for extreme-weather prediction.“Using both temperature and Z500, accuracies (recalls) with CapsNets increase to $\sim 80\%$ $(88\%)$, showing the promises of multi-modal data-driven frameworks for accurate/fast extreme weather predictions, which can augment NWP efforts in providing early warnings.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Machine Learning Methods for Weather Forecasting: A Survey

   Zhang, Liu, Zhang and Li · Atmosphere · 2025

   A survey of machine learning weather forecasting methods that reviews active research areas, notes unresolved issues such as interpretability and rare events, and offers a roadmap for future work.

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   1. UncheckedThe survey says research on global forecasting, downscaling, extreme weather and combining machine learning with physics is very active in ML weather forecasting.“Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field.”
   2. UncheckedThe survey says machine-learning weather forecasting still faces open problems, notably model interpretability and predicting rare weather events.“However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast

   Olivetti and Messori · Geoscientific model development · 2024

   The paper compares two data-driven weather models with ECMWF's physics-based forecast for temperature and wind speed extremes worldwide, finding they often compete but not uniformly.

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   1. UncheckedHow well data-driven weather models forecast extremes differs by region, type of extreme event, and how far ahead the forecast looks.“However, the performance of data-driven models varies by region, type of extreme event, and forecast lead time.”
   2. UncheckedData-driven weather models seem to forecast temperature extremes best in regions nearer the tropics and at shorter lead times.“Notably, data-driven models appear to perform best for temperature extremes in regions closer to the tropics and at shorter lead times.”

5. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Data-driven forecasts of extreme weather in East Asia: feasibility of operational use

   Oh, Bae, Son et al. · Weather and Climate Extremes · 2026

   The study tests four machine-learning weather models and their ensemble mean against Korea's operational KIM forecasts, focusing on extreme weather over East Asia in 2023 up to 10 days ahead.

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   1. UncheckedMachine-learning weather models forecast better overall when started from ERA5 reanalysis than from operational analysis, in this East Asian study.“Overall forecast skills increase when MLWP models are initialized with ERA5 reanalysis, highlighting the importance of initial conditions even in MLWP.”

6. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   How artificial intelligence is transforming weather forecasting for the future

   Huang and Bin Chen · Chinese Science Bulletin (Chinese Version) · 2024

   A review of AI in weather forecasting, arguing that AI and numerical models have complementary strengths and that the field's future lies in integrating the two.

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   1. UncheckedThe authors say AI can help build combined forecasting methods that draw on several models to give more robust weather predictions.“Additionally, AI can facilitate the development of integrated forecasting methods that leverage multiple models for more robust predictions.”