Improved modelling of non-tidal mass variations for optimized gravity field analysis

由于采样不足，无法从空间充分测量高频质量变化。它们是事先使用地球物理模型（去混叠）从测量中去除的。任何模型误差都会完全传播到最终的重力场结果中。在“理想-格雷斯”项目中，已经成功地开发了一个程序来考虑大气和海洋的不确定性。提出的工作旨在改进非潮汐短期质量变化（海洋、大气和水文）及其不确定性的建模，以优化GRACE、GOCE和下一代重力场数据分析。具体而言，预期的结果是：(1)改进的全球质量变化的地球物理模型，特别是与长期稳定性有关的模型和这些模型中应用的标准。(2)研究利用水文模型和/或GRACE日解对去混叠的意义。(3)生成细化去混叠系数的时间序列。这包括用于标准重力场分析的全局解决方案，以及SPP其他项目需要的区域改进。(4)IDEAL-GRACE项目开发的去混散和误差传播软件的审查、扩展和加速。暗示的结果将导致改进高频时变质量变化及其不确定性的建模。这不仅推动了当前重力场任务的性能，也是观测精度显著提高的下一代重力场任务需要解决的主要问题之一。

High-frequency mass variations cannot be measured adequately from space due to undersampling. They are removed from the measurements beforehand using geophysical models (de-aliasing). Any model error fully propagates into the final gravity field results. Within the IDEAL-GRACE project a procedure has been successfully developed to take atmospheric and oceanic uncertainties into account. The work proposed aims in the improved modeling of non-tidal short-term mass variations (ocean, atmosphere and hydrology) and their uncertainties in order to optimize GRACE, GOCE and next generation gravity field data analysis. In particular the expected results are: (1) Improved geophysical models of global mass variations specifically related to long-term stability and standards applied in these models. (2) Studies on the significance of the use of hydrology models and/or GRACE daily solutions for de-aliasing. (3) Generating a time series of refined de-aliasing coefficients. This includes global solutions to be applied for standard gravity field analysis as well as regional refinements needed by other projects of SPP. (4) Review, extension, speed up of de-aliasing and error-propagation software developed in the IDEAL-GRACE project. Results of IMPLY will lead to improved modelling of high frequency time-variable mass variations and their uncertainties. This not only drives the performance of current gravity field missions, but also is one of the main issues to be solved for next-generation gravity field missions with significantly improved observation accuracy.