Short note: the experimental geopotential model XGM2016
Short note: the experimental geopotential model XGM2016
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DOI：
10.1007/s00190-017-1070-6
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发表时间：
2018-04
影响因子：
4.4
通讯作者：
R. Pail;T. Fecher;D. Barnes;J. Factor;S. Holmes;T. Gruber;P. Zingerle
中科院分区：
文献类型：
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作者：
R. Pail;T. Fecher;D. Barnes;J. Factor;S. Holmes;T. Gruber;P. Zingerle
作为即将到来的组合地球引力模型2020（EGM 2020）的先驱研究，计算实验重力场模型XGM 2016，参数化为719阶的球谐级数。XGM 2016与其前身模型GOCO 05 c共享相同的组合方法（Fecher等人，Surv Geophys 38（3）：571-590，2017）。土井： 10.1007/s10712-016-9406-y ).这些模型之间的主要区别在于，XGM 2016得到了美国国家地理空间情报局（NGA）提供的改进的重力异常区域数据集的支持，与现有的组合重力场模型相比，这导致了重大升级，特别是在南美洲，非洲，亚洲部分地区和南极洲等大陆地区。相对区域加权的组合策略提供了改进的性能，在近海岸海洋区域，包括区域的高度数据大多是从以前的模式不变。比较EGM 2008和XGM 2016在度/阶数719处的累积高度异常，在非洲产生26 cm的差异，在南美洲产生40 cm的差异。产生这些差异的原因是纳入了更多的卫星数据信息，以及这些区域的地面数据有所改善。XGM 2016还产生了更平滑的平均动态地形，并显着减少了伪影，这表明海洋区域的建模得到了改进。
As a precursor study for the upcoming combined Earth Gravitational Model 2020 (EGM2020), the Experimental Gravity Field Model XGM2016, parameterized as a spherical harmonic series up to degree and order 719, is computed. XGM2016 shares the same combination methodology as its predecessor model GOCO05c (Fecher et al. in Surv Geophys 38(3): 571–590, 2017. doi: 10.1007/s10712-016-9406-y ). The main difference between these models is that XGM2016 is supported by an improved terrestrial data set ofgravity anomaly area-means provided by the United States National Geospatial-Intelligence Agency (NGA), resulting in significant upgrades compared to existing combined gravity field models, especially in continental areas such as South America, Africa, parts of Asia, and Antarctica. A combination strategy of relative regional weighting provides for improved performance in near-coastal ocean regions, including regions where the altimetric data are mostly unchanged from previous models. Comparing cumulative height anomalies, from both EGM2008 and XGM2016 at degree/order 719, yields differences of 26 cm in Africa and 40 cm in South America. These differences result from including additional information of satellite data, as well as from the improved ground data in these regions. XGM2016 also yields a smoother Mean Dynamic Topography with significantly reduced artifacts, which indicates an improved modeling of the ocean areas.