Landskron Daniel, Böhm Johannes
Technische Universität Wien, Vienna, Austria.
J Geod. 2018;92(12):1387-1399. doi: 10.1007/s00190-018-1127-1. Epub 2018 Feb 20.
Missing or incorrect consideration of azimuthal asymmetry of troposphere delays is a considerable error source in space geodetic techniques such as Global Navigation Satellite Systems (GNSS) or Very Long Baseline Interferometry (VLBI). So-called horizontal troposphere gradients are generally utilized for modeling such azimuthal variations and are particularly required for observations at low elevation angles. Apart from estimating the gradients within the data analysis, which has become common practice in space geodetic techniques, there is also the possibility to determine the gradients beforehand from different data sources than the actual observations. Using ray-tracing through Numerical Weather Models (NWMs), we determined discrete gradient values referred to as GRAD for VLBI observations, based on the standard gradient model by Chen and Herring (J Geophys Res 102(B9):20489-20502, 1997. 10.1029/97JB01739) and also for new, higher-order gradient models. These gradients are produced on the same data basis as the Vienna Mapping Functions 3 (VMF3) (Landskron and Böhm in J Geod, 2017. 10.1007/s00190-017-1066-2), so they can also be regarded as the VMF3 gradients as they are fully consistent with each other. From VLBI analyses of the Vienna VLBI and Satellite Software (VieVS), it becomes evident that baseline length repeatabilities (BLRs) are improved on average by 5% when using a priori gradients GRAD instead of estimating the gradients. The reason for this improvement is that the gradient estimation yields poor results for VLBI sessions with a small number of observations, while the GRAD a priori gradients are unaffected from this. We also developed a new empirical gradient model applicable for any time and location on Earth, which is included in the Global Pressure and Temperature 3 (GPT3) model. Although being able to describe only the systematic component of azimuthal asymmetry and no short-term variations at all, even these empirical a priori gradients slightly reduce (improve) the BLRs with respect to the estimation of gradients. In general, this paper addresses that a priori horizontal gradients are actually more important for VLBI analysis than previously assumed, as particularly the discrete model GRAD as well as the empirical model GPT3 are indeed able to refine and improve the results.
在全球导航卫星系统（GNSS）或甚长基线干涉测量（VLBI）等空间大地测量技术中，对流层延迟方位不对称性的缺失或错误考虑是一个相当大的误差源。所谓的水平对流层梯度通常用于对这种方位变化进行建模，对于低仰角观测尤为必要。除了在数据分析中估计梯度（这在空间大地测量技术中已成为常见做法）之外，还有可能事先从与实际观测不同的数据源确定梯度。通过数值天气模型（NWM）进行射线追踪，我们基于Chen和Herring（《地球物理研究杂志》102(B9):20489 - 20502，1997. 10.1029/97JB01739）的标准梯度模型以及新的高阶梯度模型，为VLBI观测确定了离散梯度值，称为GRAD。这些梯度与维也纳映射函数3（VMF3）（Landskron和Böhm在《大地测量学杂志》，2017. 10.1007/s00190 - 017 - 1066 - 2）基于相同的数据基础生成，所以它们也可被视为VMF3梯度，因为它们彼此完全一致。从维也纳VLBI和卫星软件（VieVS）的VLBI分析中可以明显看出，使用先验梯度GRAD而非估计梯度时，基线长度重复性（BLR）平均提高了5%。这种改进的原因是，对于观测次数较少的VLBI观测时段，梯度估计产生的结果较差，而GRAD先验梯度不受此影响。我们还开发了一种适用于地球上任何时间和地点的新经验梯度模型，该模型包含在全球压力和温度3（GPT3）模型中。尽管只能描述方位不对称性的系统分量且完全不能描述短期变化，但即使是这些经验先验梯度相对于梯度估计也能略微降低（改善）BLR。总体而言，本文指出先验水平梯度对于VLBI分析实际上比以前认为的更为重要，因为特别是离散模型GRAD以及经验模型GPT3确实能够细化和改善结果。
