Comment on essd-2022-150

作者：Jianping Guo, Jian Zhang, Tianmeng Chen, Kaixu Bai, Jia Shao, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason Blake Cohen, Panmao Zhai, Xiaofeng Xu, Fei Hu · 年份：2022 · DOI：10.5194/essd-2022-150-rc2 · 研究领域：Air Quality Monitoring and Forecasting、Atmospheric and Environmental Gas Dynamics、Atmospheric chemistry and aerosols

The planetary boundary layer (PBL) is the lowermost part of the troposphere that governs the exchange of momentum, mass and heat between surface and atmosphere. To date the radiosonde measurements have been extensively used to estimate PBLH; suffering from low spatial coverage and temporal resolution, the radiosonde data is incapable of providing the diurnal description of PBLH across the globe. To fill this data gap, this paper aims to produce a temporally continuous PBLH dataset during the course of a day over the global land by applying the machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3-hour and a horizontal resolution of 0.25°×0.25°. The radiosonde dataset contained around 180 million profiles over 370 stations across the globe. The machine learning model was established by taking 18 parameters derived from ERA5 reanalysis and GLDAS as input variables while the PBLH biases between radiosonde observations and ERA5 reanalysis were used as the learning targets. The input variables were presumably representative regarding the land properties, near-surface meteorological conditions, terrain elevations, lower tropospheric stabilities, and solar cycles. Once a state-of-the-art model had been trained, the model was then used to predict the PBLH bias at other grids across the globe with parameters acquired or derived from ERA5 and ...