Microsoft climate prediction model

Excited to announce the public code release of Aurora - a foundation model for atmospheric forecasting! 🌍 ⛅ Code: https://lnkd.in/dsaGx_hr Docs: https://lnkd.in/d7TQ-HTN Paper: https://lnkd.in/dBvgfPDG Aurora sets a new state-of-the-art in global weather and air quality prediction, outperforming traditional numerical models while being orders of magnitude faster. Key features: • Pretrained on diverse atmospheric data. • Fine-tuned versions for weather and air quality. • 0.1° resolution global forecasts. • Outperforms IFS-HRES and GraphCast on most metrics. The repo currently includes: • Pretrained model weights. • Fine-tuned weights for high-res weather forecasting. • Easy-to-use Python API. • Detailed documentation and examples. • Get started now with a simple example that runs Aurora on ERA5: https://lnkd.in/dnV5rR_V We hope this accelerates research into foundation models for Earth system prediction. Read the full paper here: https://lnkd.in/dBvgfPDG. Amazing effort by Cristian Bodnar, Wessel B., Ana Lucic and Megan Stanley at Microsoft Research AI for Science. #MachineLearning #WeatherForecasting

Microsoft Research just published a paper in Nature about Aurora, a foundation model trained on over a million hours of Earth system data. The idea is simple: one model, pre-trained on diverse geophysical datasets, then fine-tuned for specific tasks like air quality, wave forecasting, tropical cyclones or high-resolution weather. The results they report are strong, especially in benchmarks. Faster, lighter, and often more accurate than current operational models. But it's still early. These are "offline" experiments. Forecasting in real conditions is another story. You have to deal with multiple data sources, numerous forecast. This means choosing, based on knowledge of the quality of the different models and experience of each weather situation and microclimate... Still, it's a clear sign of where things are going. The gap between physics-based modeling and AI is closing, fast. Link to the paper in the first comment! #AI #Weather #Climate #Aurora #Forecasting

Aurora, a 1.3 billion parameter foundation model from Microsoft Research AI for Science, offers high-resolution weather and air pollution forecasts by leveraging diverse atmospheric data. It efficiently adapts to new tasks, outperforms traditional models in extreme weather prediction, and delivers rapid results, all while being computationally efficient. For municipalities, it provides rapid and accurate predictions, aiding in better decision-making for urban planning, emergency response, and environmental management, while efficiently handling extreme weather events. Paper: https://lnkd.in/dMa6Tkya Github: https://lnkd.in/dwJYA3WY Infographics: Napkin AI #AI #ClimateScience #WeatherForecasting #MunicipalPlanning #UrbanManagement #EmergencyResponse #EnvironmentalManagement #MachineLearning #SmartCities #PublicSafety