An Assessment of GPT2w Model and Fusion of a Troposphere Model with in Situ Data

HUA Zhonghao; LIU Lintao; LIANG Xinghui

doi:10.13203/j.whugis20150758

Volume 42 Issue 10

Oct. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(10): 1468-1473. > DOI: 10.13203/j.whugis20150758

HUA Zhonghao, LIU Lintao, LIANG Xinghui. An Assessment of GPT2w Model and Fusion of a Troposphere Model with in Situ Data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1468-1473. DOI: 10.13203/j.whugis20150758

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An Assessment of GPT2w Model and Fusion of a Troposphere Model with in Situ Data

1.
University of Chinese Academy of Sciences, Beijing 100049, China
2.
State Key Laboratory of Geodesy and Earth's Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China

Funds:

The Special Fund Protect of Major Scientific Instrument and Equipment Development 2011YQ120045

the Natural Science Foundation of China 41304023

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Author Bio:
HUA Zhonghao, postgraduate, specializes in the precise data analysis of GNSS and GNSS meteorology. E-mail:sucfay@gmail.com
Corresponding author:
LIU Lintao, PhD, professor. E-mail: llt@asch.whigg.ac.cn
Received Date: May 15, 2016
Published Date: October 04, 2017

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Abstract

Abstract

GPT2w is used to estimate slant tropospheric delay, and considered the best empirical model based on its nominal accuracy. Besides the model value of ZHD, this model delivers a blind value for meteorological parameters. We used the database from USNO to validate the marked accuracy of the model, and data from IGRA to access the accuracy of the blind meteorological elements. A system bias from T_m was detected. After the correction of this bias, the bias of model ZTD against the USNO ZTD rose from-1.38 mm to-0.3 mm. This paper presents a fusion of blind model and the in situ data. The input parameters of this new method are in situ P, t and hr, the corrected blind T_m and λ. This method performs better than the modified GPT2w model because of the in situ data, better than the Saast model as it profits from a improved ZWD model. Without the in situ data, the modified GPT2w is a good choice. If with available in situ data, the fusion method is recommended.
- GPT2w,
- zenith tropospheric delay,
- T_m,
- in situ meteorological data

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