Stochastic weather generators for high-frequency wind vector time series
Researchers have developed machine learning models, specifically time vector-quantized variational autoencoders, to generate realistic high-frequency wind vector time series. These generators aim to simulate minute-scale wind data, which exhibits complex diurnal patterns challenging for standard models. While the best models capture diurnal volatility, they struggle to accurately replicate extreme wind speed distributions. AI
IMPACT Provides a new tool for simulating complex wind patterns, potentially improving wind energy forecasting and wildfire spread modeling.