A UNet retrieval of tropical cyclone inner-core wind fields from microwave and infrared satellite imagery

Abstract

Abstract Deep learning frequently leverages satellite imagery to estimate key benchmarked properties of tropical cyclones (TCs) such as intensity. This study goes a step further to investigate the potential for using this two-dimensional information to produce a two-dimensional wind field product for the TC inner core. Here we train a product on flight-level in situ wind from center-crossing aircraft transects and focus on the ability to reproduce a full two-dimensional field of flight-level wind. The wind model, dubbed ‘TC2D,’ is a unique multi-branched UNet design with a loss function that efficiently compensates for the relative sparsity of labeled data. This model accurately captures many challenging radial wind profiles, including large eyewalls, profiles with secondary wind maxima and TCs in transition between various states. It performs well in a variety of environments including strong vertical wind shear. The RMS error of the estimated radius of maximum winds is 15.5 km for Category 2–5 TCs, and half of the tested cases have error less than 1.3 km. The RMS error of windspeed is 5–6 ms−1 for tropical depression to Category 1-strength TCs and 5–10 ms−1 for Category 2–5 TCs, depending on radius. The model generally lacks the ability to reproduce the storm-relative azimuthal variability of flight-level wind, but it successfully captures earth-relative variability due to the more straightforward corrections for TC translation. TC2D offers to be a good nowcasting aide to provide low-latency, TC inner core wind distribution estimates several times per day.