GPT2: Empirical slant delay model for radio space geodetic techniques.

GPT2: Empirical slant delay model for radio space geodetic techniques.
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DOI:
10.1002/grl.50288
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发表时间:
2013-03-28
影响因子:
5.2
通讯作者:
Nilsson, T.
Nilsson, T.
中科院分区:
地球科学1区
文献类型:
--
作者:
Lagler, K.;Schindelegger, M.;Boehm, J.;Krasna, H.;Nilsson, T.

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到目前为止,国家的最先进的经验倾斜延迟处理从无线电空间大地测量技术的观测模型已经提供了两个经验模型的组合。这些是GPT(全球压力和温度)和GMF(全球映射函数),两者都是基于数值天气模型的表面值的长期平均值。GPT/GMF的弱点,特别是它们有限的空间和时间变化,在很大程度上被一个新的组合模式GPT 2所消除,该模式提供了任何地点的压力,温度,直减率,水汽压和映射函数系数,所有参数的平均值,年度和半年变化的全球5°网格。GPT 2基于ERA-Interim数据,为地球物理研究带来了改进的经验倾斜延迟。与GPT/GMF相比,GPT 2相对于基于瞬时局部气压值和维也纳映射函数1的解,产生台站高度的年和半年振幅差减少40%,如一系列全球VLBI(甚长基线干涉测量)解所示。
Up to now, state-of-the-art empirical slant delay modeling for processing observations from radio space geodetic techniques has been provided by a combination of two empirical models. These are GPT (Global Pressure and Temperature) and GMF (Global Mapping Function), both operating on the basis of long-term averages of surface values from numerical weather models. Weaknesses in GPT/GMF, specifically their limited spatial and temporal variability, are largely eradicated by a new, combined model GPT2, which provides pressure, temperature, lapse rate, water vapor pressure, and mapping function coefficients at any site, resting upon a global 5° grid of mean values, annual, and semi-annual variations in all parameters. Built on ERA-Interim data, GPT2 brings forth improved empirical slant delays for geophysical studies. Compared to GPT/GMF, GPT2 yields a 40% reduction of annual and semi-annual amplitude differences in station heights with respect to a solution based on instantaneous local pressure values and the Vienna mapping functions 1, as shown with a series of global VLBI (Very Long Baseline Interferometry) solutions.
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