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https://doi.org/10.5194/gmd-2024-21
https://doi.org/10.5194/gmd-2024-21
Submitted as: development and technical paper
 | 
11 Apr 2024
Submitted as: development and technical paper |  | 11 Apr 2024
Status: this preprint is currently under review for the journal GMD.

A hybrid-grid global model for the estimation of atmospheric weighted mean temperature considering time-varying lapse rate in GNSS precipitable water vapor retrieval

Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu

Abstract. The atmospheric weighted mean temperature (Tm) is a key parameter in global navigation satellite system (GNSS) water vapor retrieval and can convert the zenith wet delay (ZWD) into precipitable water vapor (PWV). However, there are some shortcomings in the existing Tm models, such as the detailed time-varying lapse rate not being considered. Additionally, the spatiotemporal characteristics of Tm need to be further refined. Therefore, we developed a new global high-precision and high-spatiotemporal-resolution Tm model considering time-varying lapse rate using the latest European Centre for Medium-Range Weather Forecasts ReAnalysis 5 (ERA5) atmospheric reanalysis data. Firstly, a global multidimensional Tm lapse rate model (NGGTm-H model) was developed using the sliding window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situation were investigated. Finally, a hybrid-grid global Tm model considering time-varying lapse rate (NGGTm model) was developed. To verify the effectiveness of the proposed model, the NGGTm model was compared with the Bevis and GPT3 models using the Tm data recorded at 378 radiosonde stations in 2017 and the surface grid Tm data calculated from the ERA5 reanalysis data. The results show that taking the surface grid Tm data of ERA5 as reference values, the average root mean square error (RMSE) value predicted by the NGGTm model was 2.84 K, which was higher with 0.50 K, 0.18 K and 0.06 K than those of the Bevis, GPT3-5 and GPT3-1 models, respectively. Meanwhile, taking the Tm data from the radiosonde stations as the reference values, the mean bias and RMSE of the NGGTm model were 0.10 K and 3.30 K, respectively, which exhibit the best accuracy and stability among the Bevis, GPT3-5 and GPT3-1 models.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu

Status: open (until 13 Jun 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2024-21', Anonymous Referee #1, 05 May 2024 reply
    • AC1: 'Reply on RC1', Jihong Zhang, 14 May 2024 reply
      • RC2: 'Reply on AC1', Anonymous Referee #1, 21 May 2024 reply
  • CEC1: 'Comment on gmd-2024-21 - No Compliance with GMD's policy', Juan Antonio Añel, 11 May 2024 reply
    • AC2: 'Reply on CEC1', Jihong Zhang, 14 May 2024 reply
      • CEC2: 'Reply on AC2', Juan Antonio Añel, 18 May 2024 reply
        • AC3: 'Reply on CEC2', Jihong Zhang, 20 May 2024 reply
          • CEC3: 'Reply on AC3', Juan Antonio Añel, 20 May 2024 reply
            • AC4: 'Reply on CEC3', Jihong Zhang, 24 May 2024 reply
              • CEC4: 'Reply on AC4', Juan Antonio Añel, 24 May 2024 reply
                • AC5: 'Reply on CEC4', Jihong Zhang, 27 May 2024 reply
Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu
Shaofeng Xie, Jihong Zhang, Liangke Huang, Fade Chen, Yongfeng Wu, Yijie Wang, and Lilong Liu

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Short summary
We developed a new global atmospheric weighted mean temperature (Tm) model considering time-varying lapse rate. Firstly, a global multidimensional Tm lapse rate model (NGGTm-H model) was developed using the sliding window algorithm. Secondly, the daily variation characteristics of Tm and its relationships with geographical situation were investigated. Finally, a hybrid-grid global Tm model considering time-varying lapse rate (NGGTm model) was developed.