Comment on essd-2022-309

作者：Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, Jian Peng · 年份：2022 · DOI：10.5194/essd-2022-309-rc1 · 研究领域：Radiomics and Machine Learning in Medical Imaging

Abstract. The wet-bulb temperature (WBT; T W ) 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 T W 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 T W 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 T W (GSDM-WBT) from 1981 to 2020. Compared with other station-based and reanalysis-based datasets of T W , 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 T W 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).