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Estimates of the surface wind field in a tropical cyclone (TC) are required in real time by operational forecast centers to warn the public about potential impacts to life and property. In‐situ aircraft data must be adjusted from flight level to surface using wind reductions (WRs) since the aircraft cannot fly too low due to safety concerns. Current operational WRs do not capture all the variability in the TC surface wind field. In this study, an observational data set of Stepped Frequency Microwave Radiometer (SFMR) surface wind speeds that are collocated with flight‐level predictors is used to analyze the variability of WRs with respect to aircraft altitude and TC storm motion and intensity. The Surface Winds from Aircraft with a Neural Network (SWANN) model is trained on the observations with a custom loss function that prioritizes accurate prediction of relatively rare high‐wind observations and minimization of variance in the WRs. The model is capable of learning physical relationships that are consistent with theoretical understanding of the TC boundary layer. Radar‐derived wind fields at flight level and independent dropwindsonde in‐situ surface wind measurements are used to validate the SWANN model and show improvement over the current operational procedure. A test case shows that SWANN can produce a realistic asymmetric surface wind field from a radar‐derived flight‐level wind field which has a maximum wind speed similar to the operational intensity, suggesting promise for the method to lead to improved real‐time TC intensity estimation and prediction in the future. 

This study analyses Global Positioning System dropsondes to document the axisymmetric tropical cyclone (TC) boundary-layer structure, based on storm intensity. A total of 2608 dropsondes from 42 named TCs in the Atlantic basin from 1998 to 2017 are used in the composite analyses. The results show that the axisymmetric inflow layer depth, the height of maximum tangential wind speed, and the thermodynamic mixed layer depth are all shallower in more intense TCs. The results also show that more intense TCs tend to have a deep layer of the near-saturated air inside the radius of maximum wind speed (RMW). The magnitude of the radial gradient of equivalent potential temperature (θe) near the RMW correlates positively with storm intensity. Above the inflow layer, composite structures of TCs with different intensities all possess a ring of anomalously cool temperatures surrounding the warm-core, with the magnitude of the warm-core anomaly proportional to TC intensity. The boundary layer composites presented here provide a climatology of how axisymmetric TC boundary layer structure changes with intensity.