A merged hurricane boundary layer height dataset in the western Atlantic based on dropsonde measurements and ERA5 reanalysis

Abstract. The hurricane boundary layer (HBL) critically regulates exchanges of heat, moisture, and momentum between the ocean and atmosphere. Accurate estimation of HBL height (HBLH) is essential for understanding hurricane dynamics. Dropsonde observations provide high accuracy but have limited spatiotemporal coverage, whereas ERA5 reanalysis offers continuous coverage with systematic biases. This study presents a merged HBLH dataset for 75 hurricanes over the western Atlantic during 2002–2024, generated by integrating 4438 dropsonde profiles, ERA5 reanalysis, and IBTrACS hurricane records through a Random Forest machine learning framework. Nineteen input variables representing thermodynamic, dynamic, and hurricane-specific parameters were used to predict the HBLH bias between dropsondes and ERA5. The corrected dataset retains the original ERA5 resolution (0.25° × 0.25°, 1-hourly) and significantly reduces systematic errors relative to dropsonde observations. Validation shows a correlation coefficient of 0.93 with dropsonde-derived HBLH, and reductions in MAE from 544 m to 159 m and RMSE from 661 m to 246 m. The merged HBLH reproduces the radial and asymmetric structure within hurricane domains more accurately than ERA5, while providing continuous temporal and spatial coverage suitable for further analysis of HBL dynamics under hurricane conditions. The dataset is publicly available at https://doi.org/10.5281/zenodo.17196964.