2024/01/18 by Florian Achermann, Achermann, Florian, Thomas Stastny +11
Earth and Planetary Sciences · Engineering · Environmental Science · #Aerospace and Aviation Technology #Artificial Intelligence (cs.AI) #Atmospheric aerosols and clouds #FOS: Computer and information sciences #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2401.09944
openalex publication_date 2024/01/18 · openalex created_date 2024/01/20 · openalex updated_date 2026/07/28
Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation. Current weather models require too much compute and lack the necessary predictive capabilities as they are valid only at the scale of multiple kilometers and hours - much lower spatial and temporal resolutions than these applications require. Our work, for the first time, demonstrates the ability to predict low-altitude wind in real-time on limited-compute devices, from only sparse measurement data. We train a neural network, WindSeer, using only synthetic data from computational fluid dynamics simulations and show that it can successfully predict real wind fields over terrain with known topography from just a few noisy and spatially clustered wind measurements. WindSeer can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. We demonstrate that the model successfully predicts historical wind data collected by weather stations and wind measured onboard drones.