GPT和GPT2模型的偏差对定位结果的影响

Impact of Deviation of GPT Model and GPT2 Model on Positioning Results

气象模型GPT和GPT2均可用于获取测站的气压、气温等气象要素,对计算对流层延迟具有重要作用并在高精度的GPS数据分析中被广泛使用。GPT2对GPT模型精度的改进在很多文献中已经得到验证,但是目前没有相关文献对采用这两种模型获得的坐标时间序列的差异进行详尽的分析。本文分别利用气象模型GPT和GPT2处理相同的连续观测站数据,发现气压值偏差的季节性变化导致测站垂向位置偏差也产生季节性变化,是测站垂向位置"伪"年周变化信号的来源之一;同时,模型之间的气压值偏差对垂向位置的影响与测站纬度相关,表现为先验天顶延迟偏差传递进垂向位置偏差的比例随测站纬度增加而增大。

The global models of pressure and temperature GPT and GPT2 can be used to obtain meteorological elements such as pressure and temperature of GPS sites, which play an important role in calculating tropospheric delay and are widely used in high precision GPS data analysis.The improvement of GPT2 model to GPT model has been validated in many literatures while there is no detailed analysis on the deviations of coordinate time series obtained by using these two models respectively. In this paper, we use the GPT model and GPT2 model to deal with the same continuous observation data respectively. As a result, we find that the seasonal variation of the deviation of the pressure leads to the seasonal variation of the site vertical positioning estimates, which is one of the sources that lead to aliased seasonal signals.Simultaneously, the influence of the deviation of the pressure on the site vertical positioning estimates is related to the site latitude, showing that the ratio of the deviation of the priori zenith delay to the vertical position is increased as the latitude of the site increases.