GSDM-WBT: global station-based daily maximum wet-bulb temperature data for 1981–2020
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, Jian Peng
Abstract
Abstract. The wet-bulb temperature (WBT; TW) comprehensively characterizes the temperature and humidity of the thermal environment and is a relevant variable to describe the energy regulation of the human body. The daily maximum TW can be effectively used in monitoring humid heat waves and their effects on health. Because meteorological stations differ in temporal resolution and are susceptible to non-climatic influences, it is difficult to provide complete and homogeneous long-term series. In this study, based on the sub-daily station-based HadISD (Met Office Hadley Centre Integrated Surface Database) dataset and integrating the NCEP-DOE reanalysis dataset, the daily maximum TW series of 1834 stations that have passed quality control were homogenized and reconstructed using the method of Climatol. These stations form a new dataset of global station-based daily maximum TW (GSDM-WBT) from 1981 to 2020. Compared with other station-based and reanalysis-based datasets of TW, the average bias was −0.48 and 0.34 ∘C, respectively. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, and also avoids the underestimation of the TW calculated from reanalysis data. The GSDM-WBT dataset can effectively support the research on global or regional extreme heat events and humid heat waves. The dataset is available at https://doi.org/10.5281/zenodo.7014332 (Dong et al., 2022).