GitHub - google-research/weatherbenchX: A modular framework for evaluating weather forecasts A modular framework for evaluating weather forecasts - google-research/weatherbenchX

🚨 WeatherBench Update

1. WeatherBench-X, our new evaluation code, is now on GitHub: github.com/google-resea...

2. New models (plus other small updates) on the WeatherBench website: sites.research.google/weatherbench/

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Jacobian-Enforced Neural Networks (JENN) for Improved Data Assimilation Consistency in Dynamical Models Machine learning-based weather models have shown great promise in producing accurate forecasts but have struggled when applied to data assimilation tasks, unlike traditional numerical weather predicti...

🌩️ Excited to share my work on Jacobian-Enforced Neural Networks (JENN)! JENN fills the gap between ML weather models & data assimilation, enhancing sensitivity (Jacobian) accuracy without compromising forecasts. A game-changer for tools like GraphCast, Pangu & NeuralGCM! 🌍 #ML #WeatherForecasting

Oh, yes! I share your optimism. My hope is that AI can work better and better in weather forecasting as we keep honing it.

Exploring the Use of Machine Learning Weather Models in Data Assimilation The use of machine learning (ML) models in meteorology has attracted significant attention for their potential to improve weather forecasting efficiency and accuracy. GraphCast and NeuralGCM, two prom...

ML weather models like GraphCast & NeuralGCM show promise in weather forecasting, but can they be used for data assimilation? Our study looks into their tangent linear & adjoint models, finding challenges in robustness & unphysical noise. 🌦️ #AI #NWP
arxiv.org/abs/2411.14677