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 harmonic 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. Compared to the CPF approach, the IPF approach exhibits stronger signals in low-degree SHCs (i.e., degrees below 20) and lower noise in high-order SHCs (i.e., orders above 40), alongside higher signal-to-noise ratios and better agreement with CSR mascon product and NOAH model in global and basin analysis. Across the 22 largest basins worldwide, the average Nash–Sutcliffe coefficients of latitude-weighted terrestrial water storage anomalies filtered by the IPF approach relative to those derived from CSR mascon product and NOAH model are 0.90 and 0.21, significantly higher than 0.17 and − 0.71, filtered by the CPF approach. 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

从 GRACE（重力恢复和气候实验）月度重力场模型中消除条纹噪声对于准确解释时间重力变化至关重要。传统的参数滤波（CPF）方法用谐波模型表达信号分量，而忽略非周期和年际信号。为了解决这个问题，我们通过使用一阶高斯-马尔可夫过程合并那些被忽略的信号来改进 CPF 方法。采用改进的参数滤波（IPF）方法对Tongji-Grace2018模型2002年4月至2016年12月的月球谐系数（SHC）进行滤波。与CPF方法相比，IPF方法在低次SHC中表现出更强的信号（即，低于 20 度）和高阶 SHC（即，高于 40 阶）噪声较低，同时具有更高的信噪比，并且在全球和盆地分析中与 CSR mascon 产品和 NOAH 模型更好地吻合。在全球 22 个最大的流域中，通过 IPF 方法过滤的纬度加权陆地水储量异常的平均 Nash-Sutcliffe 系数相对于 CSR mascon 产品和 NOAH 模型得出的系数为 0.90 和 0.21，显着高于 0.17 和 − 0.71，通过 CPF 方法进行过滤。仿真实验进一步表明，IPF方法得到的滤波结果最接近实际信号，均方根误差降低了30.1%、25.9%、45.3%、30.9%、46.6%、32.7%、39.6%和38.2%与 CPF、DDK3、最小二乘、RMS、Gaussian 300、Fan 300、Gaussian 相比，陆地上的效率高 2.8%、54.4%、70.1%、15.3%、69.2%、46.5%、40.4% 和 23.6%分别采用 P4M6 的 300 和采用 P4M6 滤波方法的 Fan 300