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Impacts of Stochastic Modeling on GPS-Derived ZTD Estimations

机译:随机造型对GPS衍生ZTD估计的影响

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GPS-derived ZTD (Zenith Tropospheric Delay) plays a key role in near real-time weather forecasting, especially in improving the precision of Numerical Weather Prediction (NWP) models. The ZTD is usually estimated using the first-order Gauss-Markov process with a fairly large correlation, and under the assumption that all the GPS measurements, carrier phases or pseudo-ranges, have the same accuracy. However, these assumptions are unrealistic. This paper aims to investigate the impact of several stochastic modeling methods on GPS-derived ZTD estimations using Australian IGS data. The results show that the accuracy of GPS-derived ZTD can be improved using a suitable stochastic model for the GPS measurements. The stochastic model using satellite elevation angle-based cosine function is better than other investigated stochastic models. It is noted that, when different stochastic modeling strategies are used, the variations in estimated ZTD can reach as much as 1cm. This improvement of ZTD estimation is certainly critical for reliable NWP and other tropospheric delay corrections.
机译:GPS衍生ZTD(对流层延迟)起着近乎实时的天气预报了关键的作用,特别是在提高的数值天气预报(NWP)模型的精度。所述ZTD通常使用一阶高斯 - 马尔可夫过程具有相当大的相关估计,并且假设所有的GPS测量值,载波相位或伪范围,具有相同的准确度下。然而,这些假设是不现实的。本文旨在探讨的几个随机建模方法,利用澳大利亚IGS数据的GPS衍生ZTD估计的影响。结果表明,GPS得到ZTD的精度可以使用用于GPS测量的合适的随机模型得到改善。在使用随机模型为基础的角度卫星高度余弦函数比其他调查随机模型更好。值得注意的是,当使用不同的随机建模策略,估计ZTD变化可能高达至1cm。 ZTD估计这改善是可靠的数值天气预报和其他对流层延迟改正肯定是至关重要的。

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