3D modeling of different areas of China based on the 3D Surface Spline model
Feng Yan*, Huang Ya, Li Yijun, Zhang Jinyuan, and Liu Shuang
1. School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing 210044, China.
2. State Key Laboratory of Lunar and Planetary Sciences (Macau University of Science and Technology), Macau 999078, China.
3. State Key Laboratory of Space Weather, Chinese Academy of Sciences, Beijing 100190, China
Abstract：
This study creates a three-dimensional surface spline (3DSS) model of mainland China based on surface and CHAMP satellite observations. Through this model, the magnetic field analyses of domestic plateau (Qinghai–Tibet Plateau 28°N–38°N, 78°E–102°E), plain (middle and lower reaches of Yangtze River Plain 27°N–34°N, 111°E–122°E), and marine (parts of the East and South China Seas 16°N–30°N, 123°E–136°E) areas have been investigated. Single models of plateau and plain have also been created. To compare and verify results, the corresponding two-dimensional (2DTY) and three-dimensional (3DTY) Taylor polynomial models have been derived. Issues such as the removal of disturbing geomagnetic fields, the data gap between surface and satellite level, and boundary effect are all seriously considered. With an aim to evaluate the resulting model, some randomly selected points are not join the modeling, by which we thereby inspected the results in terms of residuals, change rate absolutes, and Root Mean Square Error (RMSE). Results show that except component Y, the change rate absolutes of other components are less than 1% both in domestic and single models, which means that the modeling result of 3DSS is better than the other two models. Plateau and plain 3DSS models reflect the fine distribution of the magnetic field after comparison with domestic distribution. The 3DSS model fits the plateau best, followed by the plain, while the worst fit is in the marine area. This means that the modeling precision depends mainly on the number and distribution of measuring points.
基金资助:This work was supported by the National Natural Science Foundation of China (Nos. 42030203, 41974073, and 41404053).
通讯作者: Feng Yan (frank_feng8848@163.com)
E-mail: frank_feng8848@163.com
作者简介: Feng Yan received his B.S. in Computer Science and Technology from Nanjing University in 2006 and his Ph.D. in Soil Science from Nanjing Agricultural University in 2011. He was a visiting scientist at the University of Liverpool in 2016–2017. He is currently an Associate Professor at the School of Atmospheric Physics, Nanjing University of Information Science and Technology. His research interests include geomagnetic field modeling and its applications
2024, Vol. 21 
