Articles | Volume 16, issue 1
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


Interactive discussion

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

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Jian Zhang on behalf of the Authors (02 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (05 Sep 2022) by Qingxiang Li
AR by Jian Zhang on behalf of the Authors (12 Sep 2022)
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.
Final-revised paper