A merged hurricane boundary layer height dataset based on dropsonde measurements and ERA5 reanalysis

This merged hurricane boundary layer height (HBLH) dataset is developed by integrating dropsonde measurements, ERA5 reanalysis and the International Best Track Archive for Climate Stewardship (IBTrACS) data using machine learning algorithms. The dataset spans 2002–2024 with a 1-h temporal and 0.25° × 0.25° spatial resolution, containing HBLHs within four times the radius of maximum wind (RMW) during 75 tropical cyclones with maximum sustained wind speeds exceeding 64 kt (hurricane intensity). In construction of the machine learning model, 5 parameters from IBTrACS (the RMW, the maximum sustained wind speed (MSW), the absolute distance (r), relative distance (r/RMW) and azimuth of each dropsonde relative to the hurricane center) and 14 parameters from ERA5 (HBLH, surface pressure, skin temperature, 2-m dew point temperature, 2-m temperature, surface latent heat flux, surface sensible heat flux, friction velocity, 10-m wind speed, boundary layer wind components ( and ), specific humidity (), temperature () and the lower tropospheric stability) were used as inputs, and the HBLH bias between dropsondes and ERA5 served as the target. The merged HBLH dataset is archived at hourly intervals for each hurricane.