the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Liangke Huang
Shengwei Lan
Ge Zhu
Fade Chen
Junyu Li
Lilong Liu
Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data for modeling. To address these limitations, we propose a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily-cycle variation and latitude factor of ZTD, using the sliding window algorithm based on fifth-generation European Centre for Medium Range Weather Forecasts (ERA5) atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model. The results indicate that the GGZTD-P model outperforms the GPT3 model, exhibiting 26 % and 53 % lower bias and RMS, respectively, when using radiosonde stations as reference values. Furthermore, when evaluated using MERRA-2 atmospheric reanalysis data, the GGZTD-P model consistently exhibits superior performance across various latitude regions. It’s expected that application of this new model will provide improved services for high-precision GNSS positioning and GNSS meteorology.
- Preprint
(1961 KB) - Metadata XML
- BibTeX
- EndNote
Liangke Huang et al.
Status: open (until 13 Oct 2023)
-
RC1: 'Comment on gmd-2023-139', Anonymous Referee #1, 20 Sep 2023
reply
Manuscript Number: gmd-2023-139
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
This paper introduces an empirical model designed to estimate tropospheric delay at various altitudes, employing a set of complex modeling equations to express variations in ZTD. The model is developed based on ERA5 atmospheric reanalysis data, utilizing MERRA-2 and Radiosonde data as reference values. It demonstrates enhanced accuracy when compared to the GPT3 model across different spatiotemporal resolutions on a global scale. However, the manuscript still contains several issues that require attention and improvement. Here are my comments for enhancement:
L13, 'propose' should be corrected to 'proposed'.
In the introduction, it is advisable to cite recent articles that reflect the current state of the field. You may consider adding descriptions or references.
doi: 10.1007/s00190-022-01630-z
doi: 10.1007/s00190-021-01535-3
L45, the first occurrence of 'GPT' should explicitly mention its full name.
L80, what is the accuracy of radiosonde data, please also include some references.
L98, clarify the unit of K3=375463 in line 98 of the manuscript; it appears to be a typographical error and should be K2.
In section 3.3, the vertical correction grid model has a horizontal resolution of 2°×2°, but in section 3.4, the empirical grid model has a horizontal resolution of 1°×1°. Please clarify why empirical model and vertical profile model have different resolutions.
In Figure 3, it should be noted that the presence of a daily period variations cannot be conclusively demonstrated based on only the three grid points. At least select some grid points at the eastern hemisphere or at low latitude regions.
In Figure 7, the developed model is compared with the GPT3 model, while in Figure 9, the developed model is compared with the GPT3 model with different spatial resolution. I do not understand why you compare the results to the same reference model GPT3, but with different spatial resolution. As the results show, the GPT3-1 model has almost the same RMS with GPT3-5 model.
Citation: https://doi.org/10.5194/gmd-2023-139-RC1 -
RC2: 'Comment on gmd-2023-139', Anonymous Referee #2, 23 Sep 2023
reply
This paper establishes a global empirical ZTD model considering the variations at different altitudes. The quality of the presented materials is sufficient to be published in Geoscientific Model Development, although some changes are required as explained below.
Revise the sentence in line 54 of the manuscript regarding ZTD data from the radiosonde station to specify the correct data source and avoid confusion, as radiosonde stations do not provide ZTD data directly.
In section 3.4, please provide a more detailed introduction of the GGZTD-P model, such as a description of the data used for its establishment. This would enhance the reader's understanding, as data specifics are crucial.
The full names of MERRA-2 and ZTD, mentioned in lines 69 and 86, were previously indicated when they first appeared in the preceding paragraph and need not be reemphasized.
P9, in section 4, Accuracy verification, the authors at least need to provide an explanation of how the GPT3 model was developed and disclose the data utilized in its formation. This information is crucial, especially considering the extensive use of the GPT3 model as a reference throughout the manuscript to assess the performance of their novel model estimates.
Please ensure consistency in the naming format for figures and tables throughout the document, such as "Figure.1" and "Figure 1. "
In line 311, how do you define the term "significant bias"? Is a significant bias, in your view, characterized by a statistically significant difference from an expected value, as determined through statistical testing?
Citation: https://doi.org/10.5194/gmd-2023-139-RC2 -
RC3: 'Comment on gmd-2023-139', Anonymous Referee #3, 27 Sep 2023
reply
This manuscript introduces a globally empirical ZTD model using ERA5 atmospheric reanalysis data. It offers a well-structured analysis of temporal and spatial characteristics, presenting intriguing research within the domain of high-precision tropospheric modeling. However, I have identified several minor issues that warrant attention and correction. Therefore, in preparation for potential publication, I recommend implementing the following modifications:
- In the introduction, the authors introduce only classic models. It is suggested to supplement the literature with recent global ZTD empirical models.
- In 158, "This may be due to the complex climate variations in these areas causing more dramatic ZTD variations ", are the authors sure that semi-daily period amplitude is mainly due to complex climate variations? Is there any other explanation for this phenomenon?
- In 162, please include specific references to substantiate the description and enhance its credibility.
- In 183, consider providing a more comprehensive explanation of "Hs" to ensure clarity and understanding.
- In 197, please correct the error in the expression of "ai".
- The authors need to pay attention to the format of all the images in the full manuscript, some partitions have subtitles, some do not, need to be uniform.
- In Figure 6, can the authors explain why they chose a height of 6km and not some other height?
- In Figure 8, the author needs to pay attention to the border of each small bar chart.
Citation: https://doi.org/10.5194/gmd-2023-139-RC3
Liangke Huang et al.
Liangke Huang et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 257 | 54 | 17 | 328 | 5 | 5 |
- HTML: 257
- PDF: 54
- XML: 17
- Total: 328
- BibTeX: 5
- EndNote: 5
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1