A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS
<p><span id="page2"/>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 PBL height (PBLH); s...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2024-01-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/16/1/2024/essd-16-1-2024.pdf |
| _version_ | 1797366308586651648 |
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| author | J. Guo J. Zhang J. Shao T. Chen K. Bai Y. Sun N. Li J. Wu R. Li J. Li Q. Guo J. B. Cohen P. Zhai X. Xu F. Hu |
| author_facet | J. Guo J. Zhang J. Shao T. Chen K. Bai Y. Sun N. Li J. Wu R. Li J. Li Q. Guo J. B. Cohen P. Zhai X. Xu F. Hu |
| author_sort | J. Guo |
| collection | DOAJ |
| description | <p><span id="page2"/>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 PBL height (PBLH); suffering from low spatial coverage and temporal resolution, the radiosonde data are incapable of providing a 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 machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and the Global Land Data Assimilation System (GLDAS) product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3 h and a horizontal resolution of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="cdee9653a8da296f292328b1ceedc79d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-16-1-2024-ie00001.svg" width="64pt" height="11pt" src="essd-16-1-2024-ie00001.png"/></svg:svg></span></span>. The radiosonde dataset contains 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 in terms of both magnitude and spatiotemporal variation, with a mean bias of as low as <span class="inline-formula">−0.9</span> m. The dataset and related codes are publicly available at <a href="https://doi.org/10.5281/zenodo.6498004">https://doi.org/10.5281/zenodo.6498004</a> (Guo et al., 2022), and are of significance for a multitude of scientific research endeavors and applications, including air quality, convection initiation, climate, and climate change, to name but a few.</p> |
| first_indexed | 2024-03-08T17:03:25Z |
| format | Article |
| id | doaj.art-d4461cdd7ee048afba75dbf6371a0e3a |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-03-08T17:03:25Z |
| publishDate | 2024-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-d4461cdd7ee048afba75dbf6371a0e3a2024-01-04T07:34:14ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162024-01-011611410.5194/essd-16-1-2024A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDASJ. Guo0J. Zhang1J. Shao2T. Chen3K. Bai4Y. Sun5N. Li6J. Wu7R. Li8J. Li9Q. Guo10J. B. Cohen11P. Zhai12X. Xu13F. Hu14State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaHubei Subsurface Multi-scale Imaging Key Laboratory, Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaCollege of Informatics, Huazhong Agricultural University, Wuhan 430070, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaMinistry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaMeteorological Observation Center, China Meteorological Administration, Beijing 100081, ChinaSchool of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou, ChinaState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaChina Meteorological Administration, Beijing 100081, ChinaState Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Beijing 100029, China<p><span id="page2"/>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 PBL height (PBLH); suffering from low spatial coverage and temporal resolution, the radiosonde data are incapable of providing a 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 machine learning algorithms to integrate high-resolution radiosonde measurements, ERA5 reanalysis, and the Global Land Data Assimilation System (GLDAS) product. This dataset covers the period from 2011 to 2021 with a temporal resolution of 3 h and a horizontal resolution of <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant="normal">0.25</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="64pt" height="11pt" class="svg-formula" dspmath="mathimg" md5hash="cdee9653a8da296f292328b1ceedc79d"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="essd-16-1-2024-ie00001.svg" width="64pt" height="11pt" src="essd-16-1-2024-ie00001.png"/></svg:svg></span></span>. The radiosonde dataset contains 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 in terms of both magnitude and spatiotemporal variation, with a mean bias of as low as <span class="inline-formula">−0.9</span> m. The dataset and related codes are publicly available at <a href="https://doi.org/10.5281/zenodo.6498004">https://doi.org/10.5281/zenodo.6498004</a> (Guo et al., 2022), and are of significance for a multitude of scientific research endeavors and applications, including air quality, convection initiation, climate, and climate change, to name but a few.</p>https://essd.copernicus.org/articles/16/1/2024/essd-16-1-2024.pdf |
| spellingShingle | J. Guo J. Zhang J. Shao T. Chen K. Bai Y. Sun N. Li J. Wu R. Li J. Li Q. Guo J. B. Cohen P. Zhai X. Xu F. Hu A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS Earth System Science Data |
| title | A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS |
| title_full | A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS |
| title_fullStr | A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS |
| title_full_unstemmed | A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS |
| title_short | A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS |
| title_sort | merged continental planetary boundary layer height dataset based on high resolution radiosonde measurements era5 reanalysis and gldas |
| url | https://essd.copernicus.org/articles/16/1/2024/essd-16-1-2024.pdf |
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