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Real-time high-resolution wind predictions are beneficial for variousapplications including safe manned and unmanned aviation. Current weathermodels require too much compute and lack the necessary predictive capabilitiesas they are valid only at the scale of multiple kilometers and hours - muchlower spatial and temporal resolutions than these applications require. Ourwork, for the first time, demonstrates the ability to predict low-altitude windin real-time on limited-compute devices, from only sparse measurement data. Wetrain a neural network, WindSeer, using only synthetic data from computationalfluid dynamics simulations and show that it can successfully predict real windfields over terrain with known topography from just a few noisy and spatiallyclustered wind measurements. WindSeer can generate accurate predictions atdifferent resolutions and domain sizes on previously unseen topography withoutretraining. We demonstrate that the model successfully predicts historical winddata collected by weather stations and wind measured onboard drones.

WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV