Landskron Daniel, Böhm Johannes
Technische Universität Wien, Vienna, Austria.
J Geod. 2018;92(4):349-360. doi: 10.1007/s00190-017-1066-2. Epub 2017 Sep 15.
Incorrect modeling of troposphere delays is one of the major error sources for space geodetic techniques such as Global Navigation Satellite Systems (GNSS) or Very Long Baseline Interferometry (VLBI). Over the years, many approaches have been devised which aim at mapping the delay of radio waves from zenith direction down to the observed elevation angle, so-called mapping functions. This paper contains a new approach intended to refine the currently most important discrete mapping function, the Vienna Mapping Functions 1 (VMF1), which is successively referred to as Vienna Mapping Functions 3 (VMF3). It is designed in such a way as to eliminate shortcomings in the empirical coefficients and and in the tuning for the specific elevation angle of . Ray-traced delays of the ray-tracer RADIATE serve as the basis for the calculation of new mapping function coefficients. Comparisons of modeled slant delays demonstrate the ability of VMF3 to approximate the underlying ray-traced delays more accurately than VMF1 does, in particular at low elevation angles. In other words, when requiring highest precision, VMF3 is to be preferable to VMF1. Aside from revising the discrete form of mapping functions, we also present a new empirical model named Global Pressure and Temperature 3 (GPT3) on a as well as a global grid, which is generally based on the same data. Its main components are hydrostatic and wet empirical mapping function coefficients derived from special averaging techniques of the respective (discrete) VMF3 data. In addition, GPT3 also contains a set of meteorological quantities which are adopted as they stand from their predecessor, Global Pressure and Temperature 2 wet. Thus, GPT3 represents a very comprehensive troposphere model which can be used for a series of geodetic as well as meteorological and climatological purposes and is fully consistent with VMF3.
对流层延迟的错误建模是全球导航卫星系统（GNSS）或甚长基线干涉测量（VLBI）等空间大地测量技术的主要误差源之一。多年来，人们设计了许多方法来将无线电波从天顶方向的延迟映射到观测仰角，即所谓的映射函数。本文提出了一种新方法，旨在改进当前最重要的离散映射函数——维也纳映射函数1（VMF1），该函数随后被称为维也纳映射函数3（VMF3）。其设计方式旨在消除经验系数以及针对特定仰角的调整中的缺点。光线追踪器RADIATE的光线追踪延迟用作计算新映射函数系数的基础。对模拟斜延迟的比较表明，VMF3比VMF1更能准确地逼近潜在的光线追踪延迟，特别是在低仰角时。换句话说，在需要最高精度时，VMF3比VMF1更可取。除了修订映射函数的离散形式外，我们还在全球网格以及全球网格上提出了一种名为全球压力和温度3（GPT3）的新经验模型，该模型通常基于相同的数据。其主要成分是通过对各自（离散）VMF3数据的特殊平均技术得出的静力和湿经验映射函数系数。此外，GPT3还包含一组气象量，这些气象量直接沿用其前身全球压力和温度2湿的。因此，GPT3是一个非常全面的对流层模型，可用于一系列大地测量以及气象和气候学目的，并且与VMF3完全一致。
