高分辨率的时间重力场数据产品:从1994年到2021年的月度质量网和球体波器
Metehan Uz1, Orhan Akyılmaz2, C K Shum3
1Dept. of Geomatics Eng., Istanbul Technical University, Istanbul, Turkey.
Scientific data
|January 13, 2024
概括
这项研究使用深度学习模拟了1994-2021年的全球质量异常,将气候变化洞察力延伸到GRACE任务时代之外. 这些发现增强了对地球系统变化和地质物理现象的理解.
科学领域:
- 地质物理学 地质物理学
- 气候科学 气候科学
- 地球系统科学 地球系统科学
背景情况:
- 像GRACE和GRACE-FO这样的卫星重力测量任务提供了关于地球水层,冰层和海洋质量变化的关键数据.
- 了解GRACE前时代的质量变化对于全面了解长期气候诱导的地球物理变化至关重要.
研究的目的:
- 从1994年1月到2021年1月模拟全球气候驱动的大规模异常.
- 扩大对在GRACE任务之前地质物理现象的时空变化的理解.
- 用独立的数据集验证模拟准确性.
主要方法:
- 为全球质量异常模拟开发了一种混合深度学习架构.
- 该模型使用GRACE/-FO面板数据,SLR推断引力测量和ECMWF Reanalysis-5数据进行训练.
- 模拟以1.0°×1.0°的空间分辨率和每月的时间分辨率进行.
主要成果:
- 该研究产生了一个长期 (1994-2021) 全球气候驱动的质量异常数据集.
- 内部验证使用RMSE和NSE等指标,与之前的研究进行比较.
- 外部验证包括与厄尔尼诺/拉尼纳指数,全球平均海平面和现场OBP测量进行比较.
结论:
- 开发的深度学习模型成功模拟了气候驱动的质量异常,为地球系统动力学提供了宝贵的见解.
- 扩展的数据集增强了对陆地水循环,冰块质量平衡和海平面变化的理解.
- 这项研究弥合了长期地质物理监测的差距,补充了现有的卫星数据.
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