Preprints
https://doi.org/10.5194/essd-2022-150
https://doi.org/10.5194/essd-2022-150
 
05 May 2022
05 May 2022
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS

Jianping Guo1, Jian Zhang2, Tianmeng Chen1, Kaixu Bai3, Jia Shao4, Yuping Sun1, Ning Li1, Jingyan Wu1, Rui Li5, Jian Li1, Qiyun Guo6, Jason B. Cohen7, Panmao Zhai1, Xiaofeng Xu8, and Fei Hu9 Jianping Guo et al.
  • 1State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 2Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
  • 3Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
  • 4College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
  • 5Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 6Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
  • 7School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
  • 8China Meteorological Administration, Beijing 100081, China
  • 9State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Beijing 100029, China

Abstract. 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 GLDAS. Eventually, the merged PBLH can be taken as the sum of the predicted PBLH bias and the PBLH retrieved from ERA5 reanalysis. Overall, this merged high-resolution PBLH dataset was globally consistent with the PBLH retrieved from radiosonde observations both in magnitude and spatiotemporal variation, with a mean bias of as low as –0.9 m. The dataset and related codes are publicly available at https://doi.org/10.5281/zenodo.6498004 (Guo et al., 2022), which are of significance for a multitude of scientific research and applications, including air quality, convection initiation, climate and climate change, just to name a few.

Jianping Guo et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-150', Anonymous Referee #1, 23 May 2022
  • RC2: 'Comment on essd-2022-150', Anonymous Referee #2, 08 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-150', Anonymous Referee #1, 23 May 2022
  • RC2: 'Comment on essd-2022-150', Anonymous Referee #2, 08 Jul 2022

Jianping Guo et al.

Data sets

A Harmonized Global Continental High-resolution Planetary Boundary Layer Height Dataset Covering 2017-2021 Jianping GUO; Jian ZHANG; Jia SHAO https://doi.org/10.5281/zenodo.6498004

Model code and software

A Harmonized Global Continental High-resolution Planetary Boundary Layer Height Dataset Covering 2017-2021 Jianping GUO; Jian ZHANG; Jia SHAO https://doi.org/10.5281/zenodo.6498004

Jianping Guo et al.

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Short summary
A global continental merged high-resolution (PBLH) dataset with a good accuracy compared to radiosonde is generated via machine learning algorithms, covering a time period from 2011 to 2021 with a 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input while PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.