A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data

ZHAO Qingzhi; YAO Yibin; XIN Linyang

doi:10.13203/j.whugis20190323

Volume 46 Issue 8

Aug. 2021

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Geomatics and Information Science of Wuhan University > 2021 > 46(8): 1131-1138. > DOI: 10.13203/j.whugis20190323

ZHAO Qingzhi, YAO Yibin, XIN Linyang. A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(8): 1131-1138. DOI: 10.13203/j.whugis20190323

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A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data

1.
College of Geomatics, Xi'an University of Science and Technology, Xi'an 710054, China
2.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Funds:

The China Postdoctoral Science Foundation Program 2020M673442

the National Natural Science Foundation of China 41904036

the Natural Science Basic Research Plan in Shaanxi Province of China 2020JQ-738

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Author Bio:
ZHAO Qingzhi, PhD, associate professor, specializes in GNSS water vapor inversion.E-mail: zhaoqingzhia@163.com
Received Date: August 04, 2020
Published Date: August 04, 2021

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Abstract

Abstract

Objectives GNSS tomography technique is one of the most important methods to obtain the three-dimensional water vapor information. However, due to lack of enough initial prior information in the process of building the tomography model, the design matrix of tomography model is unstable and the tomographic result is poor, which has become an urgent problem to be resolved.
Methods First of all, the grid data derived from European Center for Medium-Range Weather Forecasting (ECMWF) is used to calculate the initial value of water vapor density in every voxel of interest area. And then, sophisticating the traditional tomography modeling using the calculated initial water vapor values. Finally, the influence of weightings of different equations in tomography model on tomographic result is also considered.
Results Comparing to the traditional methods, the proposed approach can enhance the accuracy of tomographic result by 41.2%, and its root mean squared error (RMSE) decreases from 1.82 g/m³ to 1.07 g/m³. Additionally, the mean absolute error (MAE), Bias and standard deviation (STD) also show a better performance than those of the traditional methods.
Conclusions The purpose is to improve the accuracy of tomographic water vapor profiles and used to sophisticate the established tomography model.
- water vapor tomography,
- design matrix,
- European Center for Medium-Range Weather Forecasting (ECMWF),
- radiosonde

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References

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A Method to Sophisticate the Water Vapor Tomography Model by Combining the ECMWF Grid Data

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