| 融合GOCE和GRACE卫星数据的无约束重力场模型Tongji-GOGR2019S |
| |
| 引用本文： | 陈鑑华, 张兴福, 陈秋杰, 梁建青, 沈云中. 2020. 融合GOCE和GRACE卫星数据的无约束重力场模型Tongji-GOGR2019S. 地球物理学报, 63(9): 3251-3262, doi: 10.6038/cjg2020N0449 |
| |
| 作者姓名： | 陈鑑华 张兴福 陈秋杰 梁建青 沈云中 |
| |
| 作者单位： | 1. 广东工业大学测绘工程系, 广州 510006; 2. 波恩大学大地测量与地理信息学院, 波恩 53121; 3. 广州市城市规划勘测设计研究院, 广州 510060; 4. 同济大学测绘与地理信息学院, 上海 200092 |
| |
| 基金项目： | 国家自然科学基金（41674006，41731069），高分遥感测绘应用示范系统（一期），NSFC-广东联合基金（第二期）（U1501501）资助 |
| |
| 摘 要： | 本文在法方程层面融合GOCE卫星的Vxx、Vyy、Vzz和Vxz重力梯度分量观测数据和GRACE卫星观测数据，采用直接法解算了220阶次的重力场模型Tongji-GOGR2019S.首先利用ⅡR带通滤波器在5~41 mHz的重力梯度带宽范围内对约24个月的GOCE重力梯度观测方程进行无相移滤波处理，并组成解算220阶次重力场模型的法方程，各梯度分量根据相对于参考模型统计精度进行定权；然后与13.5 a GRACE数据建立的180阶次Tongji-Grace02s重力场模型的法方程进行叠加，解算了220阶次的无约束纯卫星重力场模型Tongji-GOGR2019S.利用EIGEN-6C4重力场模型、GNSS/水准数据、DTU15重力异常数据以及欧洲区域似大地水准面模型EGG2015等数据对Tongji-GOGR2019S模型精度进行全面的检核评定，结果表明：引入GOCE卫星梯度数据后，高于72阶的位系数精度优于Tongji-Grace02s模型，Tongji-GOGR2019S模型的整体精度接近同阶次的DIR-R6等GOCE卫星第6代模型.
|
| 关 键 词： | 全球重力场模型 GOCE GRACE Tongji-GOGR2019S |
| 收稿时间： | 2019-12-20 |
| 修稿时间： | 2020-04-21 |
Unconstrained gravity field model Tongji-GOGR2019S derived from GOCE and GRACE data |
| |
| CHEN JianHua, ZHANG XingFu, CHEN QiuJie, LIANG JianQing, SHEN YunZhong. 2020. Unconstrained gravity field model Tongji-GOGR2019S derived from GOCE and GRACE data. Chinese Journal of Geophysics (in Chinese), 63(9): 3251-3262, doi: 10.6038/cjg2020N0449 |
| |
| Authors: | CHEN JianHua ZHANG XingFu CHEN QiuJie LIANG JianQing SHEN YunZhong |
| |
| Affiliation: | 1. Department of Surveying and Mapping, Guangdong University of Technology, Guangzhou 510006, China; 2. Institute of Geodesy and Geo information, University of Bonn, Bonn 53121, Germany; 3. Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China; 4. College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China |
| |
| Abstract: | In this study, a static gravity field model Tongji-GOGR2019S up to degree and order 220 was derived from a combination of GOCE and GRACE observations on normal equation level. When generating the corresponding normal equation from GOCE, the direct approach was used to process approximately two years of gradient component data (Vxx, Vyy, Vzz and Vxz). Prior to deriving the GOCE normal equation, a band-pass ⅡR filter was applied to decorrelate both sides of SGG observation equation in the frequency band between 5 mHz and 41 mHz. To derive the normal equation, a data weighting technique was employed for each gradient component according to the corresponding standard deviation w.r.t. the a priori gravity field model. Further incoporating the GRACE normal equation regarding Tongji-Grace02s up to degree and order 180 from 13.5-year GRACE data into the derived GOCE normal equation up to degree and order 220, we successfully produced Tongji-GOGR2019S complete to degree and order 220. To comprehensively evaluate the quality of the derived model, Tongji-GOGR2019S was compared to the state-of-the-art gravity field model EIGEN-6C4 as well as geoid model EGG2015, and validated with GNSS/leveling data and DTU15 marine gravity data. The analyses allow us to derive the following conclusions:due to the use of GOCE data, the accuracy of Tongji-GOGR2019S model has been effectively improved beyond degree 72, and the accuracy of Tongji-GOGR2019S model is closer to the GOCE R6 models at the same degree. |
| |
| Keywords: | Global gravity field model GOCE GRACE Tongji-GOGR2019S |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《地球物理学报》浏览原始摘要信息 |
|
点击此处可从《地球物理学报》下载免费的PDF全文 |
|
