WindSeer: Real-time volumetric wind prediction over complex terrain
  aboard a small UAV
Florian Achermann Thomas Stastny Bogdan Danciu Andrey Kolobov Jen Jen Chung Roland Siegwart Nicholas Lawrance
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.