Nowcasting of Tropical Cyclone Wind Fields and Intensity from Geostationary Imagery

High-resolution tropical cyclone surface wind fields derived from synthetic aperture radar (SAR) observations are important for storm track and intensity modelling, but remain spatially and temporally sparse and unavailable near-real-time. Geostationary satellites measure cloud tops continuously in space and time, but do not directly resolve surface winds. Here, we present simple neural networks to predict 2D wind fields and storm-scale intensity metrics from geostationary imagery. Through a series of ablation studies, we find that joint prediction of 2D fields and scalar metrics from the model latent space improves the retrieval of the (radius of) maximum wind, especially for rapid intensification events.