five

The experimental gravity field model XGM2019e

收藏
DataCite Commons2025-12-10 更新2025-04-15 收录
下载链接:
https://dataservices.gfz.de/icgem/showshort.php?id=escidoc:4529896
下载链接
链接失效反馈
官方服务:
资源简介:
XGM2019e is a combined global gravity field model represented through spheroidal harmonics up to d/o 5399, corresponding to a spatial resolution of 2’ (~4 km). As data sources it includes the satellite model GOCO06s in the longer wavelength area combined with terrestrial measurements for the shorter wavelengths. The terrestrial data itself consists over land and ocean of gravity anomalies provided by courtesy of NGA (identical to XGM2016, having a resolution of 15’) augmented with topographically derived gravity over land (EARTH2014). Over the oceans, gravity anomalies derived from satellite altimetry are used (DTU13, in consistency with the NGA dataset). The combination of the satellite data with the terrestrial observations is performed by using full normal equations up to d/o 719 (15’). Beyond d/o 719, a block-diagonal least-squares solution is calculated for the high-resolution terrestrial data (from topography and altimetry). All calculations are performed in the spheroidal harmonic domain. In the spectral band up to d/o 719 the new model shows over land a slightly improved behavior over preceding models such as XGM2016, EIGEN6c4 or EGM2008 when comparing it to independent GPS leveling data. Over land and in the spectral range above d/o 719 the accuracy of XGM2019e suffers from the sole use of topographic forward modelling; Hence, errors are increased in well-surveyed areas compared to models containing real gravity data, e.g. EIGEN6c4 or EGM2008. However, the performance of XGM2019e can be considered as globally more homogeneous and independent from existing high resolution global models. Over the oceans the model exhibits an improved performance throughout the complete spectrum (equal or better than preceding models).
提供机构:
GFZ Data Services
创建时间:
2019-09-24
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作