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 paper
| 
04 Jan 2024
Data description paper |
| 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
7 citations as recorded by crossref.
- Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang 10.5194/gmd-17-6319-2024
- An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles F. Mashao et al. 10.3390/atmos15030387
- Climatology, trends, and variability of planetary boundary layer height over India using high-resolution Indian reanalysis K. Shukla et al. 10.1007/s00704-024-05102-6
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
- Long term trends in global air pollution potential and its application to ventilation corridors H. Kannemadugu et al. 10.1007/s11869-024-01563-w
- Atmospheric Refraction Delay Toward Millimeter‐Level Lunar Laser Ranging: Correcting the Temperature‐Induced Error With Real‐Time and Co‐Located Lidar Measurements Y. He et al. 10.1029/2023JD039579
5 citations as recorded by crossref.
- Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains T. Su & Y. Zhang 10.5194/gmd-17-6319-2024
- An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles F. Mashao et al. 10.3390/atmos15030387
- Climatology, trends, and variability of planetary boundary layer height over India using high-resolution Indian reanalysis K. Shukla et al. 10.1007/s00704-024-05102-6
- The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques L. Canché-Cab et al. 10.1007/s10462-024-10962-5
- Deep-learning-derived planetary boundary layer height from conventional meteorological measurements T. Su & Y. Zhang 10.5194/acp-24-6477-2024
2 citations as recorded by crossref.
- Long term trends in global air pollution potential and its application to ventilation corridors H. Kannemadugu et al. 10.1007/s11869-024-01563-w
- Atmospheric Refraction Delay Toward Millimeter‐Level Lunar Laser Ranging: Correcting the Temperature‐Induced Error With Real‐Time and Co‐Located Lidar Measurements Y. He et al. 10.1029/2023JD039579
Latest update: 14 Nov 2024
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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