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WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spatial and temporal resolution than the best physics-based models and they are exclusively initialized with and trained on analysis data. As a result, they cannot directly make use of observations, and any biases in the analysis are inherited by the forecast. WeatherNext 3 addresses these shortcomings and establishes a new state-of-the-art for probabilistic medium-range forecasting skill. First, WeatherNext 3 generates n
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T09:30:21.000Z
First collected: 2026-09-21T04:51:57.792Z. This is not the publication date.