Satellite scatterometer data can be used directly for short-term wind forecasting via deep learning, achieving 23% better accuracy than traditional weather models at 1-hour lead times—opening a new approach to renewable energy forecasting.
WindCastNet is a machine learning model that forecasts offshore wind speed and direction using satellite scatterometer data instead of traditional weather models. It handles irregular satellite observations from multiple sources and predicts wind fields 1-2 hours ahead, outperforming standard numerical weather prediction models for short-term forecasts critical to offshore wind farm operations.