Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
04 Jan 2024
Data description article |
| 04 Jan 2024
| 04 Jan 2024
A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS
Jianping Guo
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Jian Zhang
CORRESPONDING AUTHOR
Hubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Jia Shao
College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
Tianmeng Chen
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Kaixu Bai
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yuping Sun
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Ning Li
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Jingyan Wu
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Rui Li
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Jian Li
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Qiyun Guo
Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
Jason B. Cohen
School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, China
Panmao Zhai
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
Xiaofeng Xu
China Meteorological Administration, Beijing 100081, China
Fei Hu
CORRESPONDING AUTHOR
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Beijing 100029, China
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Cited
25 citations as recorded by crossref.
- Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang
- Comparative Analysis of Planetary Boundary Layer Heights During the BELLA CIAO Measurement Campaign in Italy A. Salcedo-Bosch et al.
- Surface-observation-constrained high-frequency coal mine methane emissions in Shanxi, China, reveal more emissions than inventories, consistent with satellite inversion F. Lu et al.
- Numerical analysis of aerosol-radiation-cloud interactions impacts on surface ozone during PM2.5-O3 compound pollution episodes in the Beijing-Tianjin-Hebei and Yangtze River Delta, China Y. Zhao et al.
- Climatology, trends, and variability of planetary boundary layer height over India using high-resolution Indian reanalysis K. Shukla et al.
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al.
- Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data Z. Xu et al.
- Estimation of Surface PM2.5 Concentration from Satellite Aerosol Optical Depth Using a Constrained Observation-Based Model O. Fawole et al.
- Improved estimation of diurnal variations in near-global PBLH through a hybrid WCT and transfer learning approach Y. Li et al.
- Retrieval of planetary boundary layer height from CALIPSO satellite observations using a machine learning approach A. Salcedo-Bosch et al.
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang
- Monitoring the Atmospheric Boundary Layer Height Using Ceilometer Lidar Over the Northeast India P. Sahu et al.
- Performance evaluation of CMIP6 global climate models using ERA5 over Indian Monsoon Region A. Sreekumar & P. Valayamkunnath
- A Bayesian PM2.5 model incorporating planetary boundary layer height variability in Tabriz, Iran A. Shikhesmailie et al.
- An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles F. Mashao et al.
- Retrieving Boundary Layer Height Using Doppler Wind Lidar and Microwave Radiometer in Beijing Under Varying Weather Conditions C. Liu et al.
- Sources, concentrations, and seasonal variations of VOC and aerosol particles in downtown Munich in 2023/2024 Y. Li et al.
- NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al.
- Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment A. Upreti et al.
- Observational study of atmospheric boundary layer height in Hong Kong based on 20-year multi-source measurements Y. Xue et al.
- Air pollution in the shadow of global crises: lessons from a small city in the Western Balkans A. Nuredini et al.
- Resolution dependence of the turbulent atmospheric boundary layer in global storm‐resolving climate simulations L. Huusko et al.
- Revealing the Drivers of Turbulence Anisotropy over Flat and Complex Terrain: An Interpretable Machine Learning Approach S. Mosso et al.
- Assessing the effectiveness of convective boundary layer height estimation using flight data and ERA5 profiles in the Amazon biome R. Carneiro et al.
- Influence of atmospheric boundary-layer dynamics on air quality of the middle- and high-density urban areas of Colombia L. Hernández Beleño et al.
25 citations as recorded by crossref.
- Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang
- Comparative Analysis of Planetary Boundary Layer Heights During the BELLA CIAO Measurement Campaign in Italy A. Salcedo-Bosch et al.
- Surface-observation-constrained high-frequency coal mine methane emissions in Shanxi, China, reveal more emissions than inventories, consistent with satellite inversion F. Lu et al.
- Numerical analysis of aerosol-radiation-cloud interactions impacts on surface ozone during PM2.5-O3 compound pollution episodes in the Beijing-Tianjin-Hebei and Yangtze River Delta, China Y. Zhao et al.
- Climatology, trends, and variability of planetary boundary layer height over India using high-resolution Indian reanalysis K. Shukla et al.
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al.
- Global tropical cyclone size and intensity reconstruction dataset for 1959–2022 based on IBTrACS and ERA5 data Z. Xu et al.
- Estimation of Surface PM2.5 Concentration from Satellite Aerosol Optical Depth Using a Constrained Observation-Based Model O. Fawole et al.
- Improved estimation of diurnal variations in near-global PBLH through a hybrid WCT and transfer learning approach Y. Li et al.
- Retrieval of planetary boundary layer height from CALIPSO satellite observations using a machine learning approach A. Salcedo-Bosch et al.
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang
- Monitoring the Atmospheric Boundary Layer Height Using Ceilometer Lidar Over the Northeast India P. Sahu et al.
- Performance evaluation of CMIP6 global climate models using ERA5 over Indian Monsoon Region A. Sreekumar & P. Valayamkunnath
- A Bayesian PM2.5 model incorporating planetary boundary layer height variability in Tabriz, Iran A. Shikhesmailie et al.
- An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles F. Mashao et al.
- Retrieving Boundary Layer Height Using Doppler Wind Lidar and Microwave Radiometer in Beijing Under Varying Weather Conditions C. Liu et al.
- Sources, concentrations, and seasonal variations of VOC and aerosol particles in downtown Munich in 2023/2024 Y. Li et al.
- NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations L. Kuhn et al.
- Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment A. Upreti et al.
- Observational study of atmospheric boundary layer height in Hong Kong based on 20-year multi-source measurements Y. Xue et al.
- Air pollution in the shadow of global crises: lessons from a small city in the Western Balkans A. Nuredini et al.
- Resolution dependence of the turbulent atmospheric boundary layer in global storm‐resolving climate simulations L. Huusko et al.
- Revealing the Drivers of Turbulence Anisotropy over Flat and Complex Terrain: An Interpretable Machine Learning Approach S. Mosso et al.
- Assessing the effectiveness of convective boundary layer height estimation using flight data and ERA5 profiles in the Amazon biome R. Carneiro et al.
- Influence of atmospheric boundary-layer dynamics on air quality of the middle- and high-density urban areas of Colombia L. Hernández Beleño et al.
Saved (final revised paper)
Latest update: 06 May 2026
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 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, with 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.
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to...
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