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The paper proposes Neural Tangent Kernel-based uncertainty quantification for deterministic deep learning weather models, achieving sharper adaptive prediction intervals during extreme events without retraining.
Introduces AdaWeather, an adaptive framework that combines multiple probabilistic weather forecasts using machine learning and mixture of experts, achieving logarithmic regret compared to the best static mixture of experts and showing empirical improvements in temperature forecasting.
Windborne Systems launched WeatherMesh 6, an AI weather forecasting model that claims to outperform the European Centre for Medium-Range Weather Forecasting (ECMWF) in accuracy and frequency, thanks to direct ingestion of sensor data from its balloons.
NOAA predicts a below-average 2026 Atlantic hurricane season due to El Niño, but warns that even quiet years can produce catastrophic storms. The agency is deploying AI weather models developed with Google DeepMind to improve hurricane track predictions.
Mosaic is a probabilistic weather model that matches state-of-the-art skill while generating a 24-member, 10-day global forecast in under 12 seconds on a single H100.
WeatherNext, an AI model from Google DeepMind, helped the National Hurricane Center predict Hurricane Melissa's rapid intensification and Category 5 landfall in Jamaica five days in advance, enabling earlier warnings and evacuations.
This paper introduces SynopticBench, a dataset of 1.3M+ weather forecast discussions paired with meteorological images, and SPACE, a novel evaluation framework for assessing VLM-generated weather forecasts.
Google DeepMind's WeatherNext AI model accurately predicted the intensification of 2025's Hurricane 'Melissa' into a Category 5 hurricane and its landfall in Jamaica three days in advance, issuing early warnings that likely saved many lives.