Preprints
https://doi.org/10.5194/gmd-2023-201
https://doi.org/10.5194/gmd-2023-201
Submitted as: model evaluation paper
 | 
05 Dec 2023
Submitted as: model evaluation paper |  | 05 Dec 2023
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

A Grid Model for Vertical Correction of Precipitable Water Vapor over the Chinese Mainland and Surrounding Areas Using Random Forest

Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Hang, and Feijuan Li

Abstract. Various ground-based observing techniques provide precipitable water vapor (PWV) products with different spatial resolutions. To effectively integrate these products, especially in terms of vertical orientation, spatial interpolation is essential. In this context, we have developed a model to characterize PWV variation with altitude in the study area. Our model, known as RF-PWV (a PWV vertical correction grid model with a 1° x 1° resolution), is constructed using random forest based on the relationship between PWV differences from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) monthly average hourly data and height differences and time. When validated against 1-h ERA5 PWV profiles, RF-PWV exhibits a 99.84 % reduction in Bias and a 63.41 % decrease in RMSE compared to the most recent model, C-PWVC1. Furthermore, when validated against radiosonde data, RF-PWV shows a 96.36 % reduction in Bias and a 5 % decrease in RMSE compared to C-PWVC1. Additionally, RF-PWV outperforms C-PWVC1 in terms of resistance to seasonal and height differences interference. The model eliminates the need for meteorological parameters, allowing for high-precision PWV vertical correction by inputting only time and height differences. Consequently, RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.

Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Hang, and Feijuan Li

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-201', Anonymous Referee #1, 13 Dec 2023
    • AC2: 'Reply on RC1', Junyu Li, 06 Feb 2024
  • RC2: 'Comment on gmd-2023-201', Anonymous Referee #2, 05 Jan 2024
    • AC1: 'Reply on RC2', Junyu Li, 06 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-201', Anonymous Referee #1, 13 Dec 2023
    • AC2: 'Reply on RC1', Junyu Li, 06 Feb 2024
  • RC2: 'Comment on gmd-2023-201', Anonymous Referee #2, 05 Jan 2024
    • AC1: 'Reply on RC2', Junyu Li, 06 Feb 2024
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Hang, and Feijuan Li
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Hang, and Feijuan Li

Viewed

Total article views: 236 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
174 43 19 236 10 10
  • HTML: 174
  • PDF: 43
  • XML: 19
  • Total: 236
  • BibTeX: 10
  • EndNote: 10
Views and downloads (calculated since 05 Dec 2023)
Cumulative views and downloads (calculated since 05 Dec 2023)

Viewed (geographical distribution)

Total article views: 229 (including HTML, PDF, and XML) Thereof 229 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Feb 2024
Download
Short summary
In this study, we have developed a model (RF-PWV) to characterize PWV variation with altitude in the study area. The RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.