Articles | Volume 16, issue 1
https://doi.org/10.5194/essd-16-1-2024
https://doi.org/10.5194/essd-16-1-2024
Data description paper
 | 
04 Jan 2024
Data description paper |  | 04 Jan 2024

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

Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu

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Cited articles

Anderson, P. S: Measurement of Prandtl number as a function of Richardson number avoiding self-correlation, Bound.-Lay. Meteorol., 131, 345–362, https://doi.org/10.1007/s10546-009-9376-4, 2009. 
Baklanov, A. A., Grisogono, B., Bornstein, R., Mahrt, L., Zilitinkevich, S. S., Taylor, P., Larsen, S. E., Rotach, M. W., and Fernando, H. J. S.: The nature, theory, and modeling of atmospheric planetary boundary layers, B. Am. Meteorol. Soc., 92, 123–128, https://doi.org/10.1175/2010BAMS2797.1, 2011. 
Basha, G. and Ratnam, M. V.: Identification of atmospheric boundary layer height over a tropical station using high-resolution radiosonde refractivity profiles: Comparison with GPS radio occultation measurements, J. Geophys. Res.-Atmos., 114, D16101, https://doi.org/10.1029/2008JD011692, 2009. 
Chan, K. M. and Wood, R.: The seasonal cycle of planetary boundary layer depth determined using COSMIC radio occultation data, J. Geophys. Res.-Atmos., 118, 12422–12434, https://doi.org/10.1002/2013JD020147, 2013. 
Cooper, D. I. and Eichinger, W. E.: Structure of the atmosphere in an urban planetary boundary layer from lidar and radiosonde observations, J. Geophys. Res.-Atmos., 99, 22937–22948, https://doi.org/10.1029/94JD01944, 1994. 
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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.
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