Extreme Weather Forecasting

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14 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Beyond the record: evaluating probabilistic AI weather forecasts deep in the tail
Oct 18, 2026Bastiaan Van Velthoven, Cas Decancq, Diego G. Miralles +2Weather Forecasting Probabilistic Forecasting
Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
Oct 15, 2026Nathan Chalumeau, Xavier Renard, Remi Meynadier +3Tropical Cyclone Intensity Forecasting Weather Forecasting
INRIA, Paris, France · AXA, Paris, France · Sorbonne Université, CNRS, LIP6, F-75005 Paris, France +1
MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation
Sep 28, 2026Ning Wang, Zuliang Fang, Weixin Jin +19Precipitation Forecasting Probabilistic Forecasting
Microsoft Corporation. · City University of Hong Kong. · University of Cambridge.
Global Evaluation of AI and NWP Precipitation Forecasts During Atmospheric River Events
Sep 26, 2026Marina Vicens-Miquel, Taylor Mandelbaum, Amy McGovern +2Precipitation Forecasting Weather Forecasting
School of Meteorology and School of Computer Science, University of Oklahoma, Norman, 73072, OK, USA. · NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography, Norman, 73072, OK, USA. · Brightband, San Francisco, CA, USA.
Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting
Aug 31, 2026Dohyun Park, Changhoon Song, Tengyuan Chang +2Precipitation Forecasting Probabilistic Forecasting
Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea. · Research Institute of Mathematics, Seoul National University, Seoul,2026 Republic of Korea. · Department of Environmental Management, Seoul National University, Seoul, Republic of Korea.
Do AI weather models miss extremes?
Jul 31, 2026Marvin Vincent Gabler, Roberto Molinaro, Niall Siegenheim +5Extreme Weather Forecasting Weather Forecasting
Jua.ai AG
Weather Emulators at the Frontier of Heat Extremes Predictability
Jul 30, 2026Cas Decancq, Thomas Mortier, Jessica Keune +1Weather Forecasting Extreme Weather Forecasting
1Hydro-Climate Extremes Lab (H-CEL), Department of Environment, Ghent University, Ghent, Belgium. · 2European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, United Kingdom.
SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents
Jul 27, 2026Hang Ni, Weijia Zhang, Fan Liu +2AI Agent Benchmarks Extreme Weather Forecasting
Artificial Intelligence Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China · Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong, China · Otto Poon Center for Climate Resilience and Sustainability, The World Sustainable Development Institute.
TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling
Jul 14, 2026Songru Yang, Zili Liu, Tao Han +7Long-Term Time Series Forecasting Extreme Weather Forecasting
Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University, and with the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education, Beihang University, Beijing 100191, China · Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China · Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China +1
Scalable Uncertainty Quantification for Extreme Weather Forecasting via Empirical Neural Tangent Kernels
Jun 1, 2026Jose Marie Antonio Miñoza, Rex Gregor Laylo, Sebastian C. IbañezUncertainty Quantification Independent Component Analysis
Center for AI Research · Department of Education · Makati, Philippines
RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
May 24, 2026Ruize Li, Zhibin Wen, Tao Han +5Benchmark Design Forecasting Benchmarks
The Hong Kong University of Science and Technology · Nanjing University · Southern University of Science and Technology +2
Extreme Weather Bench: A framework and benchmark for evaluation of high-impact weather
May 1, 2026Amy McGovern, Taylor Mandelbaum, Daniel Rothenberg +6Forecasting Benchmarks Extreme Weather Forecasting
Brightband, San Francisco, CA, USA. · School of Meteorology and School of Computer Science, University of Oklahoma, Norman, 73072, OK, USA. · Bureau of Meteorology, Australia. +5
Target Concept Tuning Improves Extreme Weather Forecasting
Mar 17, 2026Shijie Ren, Xinyue Gu, Ziheng Peng +6Extreme Weather Forecasting Weather Forecasting
Alibaba Group, Hangzhou, China
Developing Machine Learning-Based Watch-to-Warning Severe Weather Guidance from the Warn-on-Forecast System
Mar 10, 2026Montgomery Flora, Samuel Varga, Corey Potvin +1Extreme Weather Forecasting Weather Forecasting
Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma · NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma · AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography +5

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Beyond the record: evaluating probabilistic AI weather forecasts deep in the tail

Bastiaan Van Velthoven, Cas Decancq, Diego G. Miralles, Stijn Luca+1

Weather Forecasting Probabilistic Forecasting Extreme Weather Forecasting

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Can probabilistic artificial intelligence (AI) weather forecasts retain skill beyond their training domain? We compare four state-of-the-art AI models (ArchesWeatherGen, U-Cast, WeatherNext2 and GenCast-mini) with the physics-based IFS-ENS over land in 2022, at 1.5° resolution and lead times of 2--10 days. Monthly ERA5 records from 1979--2021 define common historical boundaries, and we characterize new records set in 2022 by their depth, i.e., the exceedance margin relative to local historical variability. Two proper scores, record-threshold weighted CRPS and Brier score, assess the magnitude and probability of record exceedances across all eligible cases, retaining both missed extremes and false alarms. Verified against ERA5, AI models outperform IFS-ENS for heat and cold records at almost every lead and depth. For heat, the advantage strengthens with depth, partly because IFS-ENS over-forecasts deep records, and largely persists for deeper records under HRES-fc0 verification. For wind, gains erode for deeper records and longer leads. Better tail scores can nevertheless coexist with aggregate probability deficits or surpluses, and some improvements arise chiefly on non-exceedances. Increasing underprediction on progressively more extreme observed subsets can also arise through outcome selection, even for an ideal conditional-mean forecast. These findings establish that native probabilistic AI ensembles retain, and for heat increase, their advantage over IFS-ENS as thresholds move beyond local records, although all advantages narrow with lead time. They caution against generalizing the extrapolation failure reported for deterministic AI forecasts to probabilistic systems, and motivate common verification conventions that separate comparative tail skill, performance on realized extremes, and probabilistic calibration.