Removing stripe noise from the GRACE (Gravity Recovery and Climate Experiment) monthly gravity field model is crucial for accurately interpreting temporal gravity variations. The Conventional Parameter Filtering (CPF) approach expresses the signal components with a harnizij monic model while neglecting non-periodic and interannual signals. To address this issue, we improve the CPF approach by incorporating those ignored signals using a First-Order Gauss-Markov process. The Improved Parameter Filtering (IPF) approach is used to filter the monthly Spherical Harmonic Coefficients (SHCs) of the Tongji-Grace2018 model from April 2002 to December 2016. Simulation experiments further demonstrate that the IPF approach yields the filtered results closest to the actual signals, reducing root mean square errors by 30.1%, 25.9%, 45.3%, 30.9%, 46.6%, 32.7%, 39.6%, and 38.2% over land, and 2.8%, 54.4%, 70.1%, 15.3%, 69.2%, 46.5%, 40.4%, and 23.6% over the ocean, compared to CPF, DDK3, Least-Square, RMS, Gaussian 300, Fan 300, Gaussian 300 with P4M6, and Fan 300 with P4M6 filtering approaches, respectively.

Contact: 2011480@tongji.edu.cn

Data and codes linkage: LinZhangTJ/Improved_PF_code_and_result (github.com)