GRACE和GRACE-FO缺失数据重建研究进展

Research progress in the reconstruction of GRACE and GRACE-FO missing data

GRACE和GRACE-FO重力卫星为获取地球重力场的长期时变特征提供了宝贵的观测数据.然而,由于卫星电池问题、载荷校准误差以及GRACE与GRACE-FO任务间较长的时间间隔导致GRACE和GRACE-FO观测的时变重力序列出现了缺失或间断,从而影响了结果的连续性及完整性.本文在介绍GRACE和GRACE-FO任务、数据产品及数据缺失情况的基础上,将GRACE/GRACE-FO缺失数据重建方法分为两大类,即基于数理统计方法的数据重建和基于辅助信息的数据重建.评述了以上两类重建方法的现状及其进展,比较了各方法的优缺点、应用领域及局限性.以长江流域为例,对GRACE反演的陆地水储量时间序列重建方法进行了分析和预测,结果显示基于经验模态分解的长短时记忆法重建效果最好.综上,虽然基于数理统计方法的GRACE/GRACE-FO缺失数据重建较为简单方便,但利用深度学习并结合各种辅助数据的缺失数据重建结果质量更高.本文对后续GRACE/GRACE-FO数据缺失数据填补方法选择提供了借鉴,并为时变卫星重力后续应用与研究提供了有益的参考.

The GRACE(Gravity Recovery and Climate Experiment)satellite mission,a collaboration between NASA and the German Aerospace Center,was complemented by the launch of its successor,GRACE Follow-On(GRACE-FO),in May 2018.These satellites have crucially contributed to our understanding of Earth,s long-term gravitational variations.However,gaps and interruptions in the time-variable gravity field series have arisen due to satellite battery issues,payload calibration errors,and the extended gap between the GRACE and GRACE-FO missions,affecting the continuity and completeness of the data.This paper provides an overview of the GRACE and GRACE-FO missions,data products,and the circumstances of data gaps.It categorizes the reconstruction methods for missing GRACE/GRACE-FO data into two main types:those based on mathematical statistics,the paper focuses on Singular Spectrum Analysis(SSA)and Least Squares Harmonic Analysis(LS-HE),comparing their applicability,strengths,and weaknesses with other methods such as the Autoregressive Moving Average Model(ARMA)and Multi-channel Singular Spectrum Analysis(MSSA).And those using auxiliary information,which employ other satellite data(like GNSS,Swarm,and SLR)and climate and hydrological data,often based on empirical regression relationships or deep learning.This paper evaluates these methods,comparing their applicability,strengths,and limitations,and presents a case study in the Yangtze River Basin using a combination of Empirical Mode Decomposition(EMD)and Long Short-Term Memory(LSTM),showing superior results over methods like Support Vector Machine(SVM),Random Forest(RF),and Iterative Singular Spectrum Analysis(ISSA).In conclusion,while mathematical statistical methods offer simplicity and low computational requirements,deep learning combined with various auxiliary data yields higher quality reconstruction results.In recent years,research both domestically and internationally in this field has also primarily focused on data reconstruction using various deep learning algorithms in conjunction with auxiliary information.The paper contributes to the ongoing research in this field,focusing on deep learning algorithms combined with surface mass models,climate,and hydrological data for data reconstruction,and provides insights for future approaches in filling data gaps for GRACE/GRACE-FO,enhancing the application and research in time-variable satellite gravimetry.