中文摘要：

由于GRACE卫星数据解算的时变重力场模型中高阶位系数存在误差,这些误差在重力异常图中表现为南北向的条带噪声,在应用GRACE时变重力场数据时必须进行滤波.本文在空间域引入了一种有效消除GRACE时变重力场条带噪声的平滑先验信息方法,并将其与目前常用的高斯滤波和去相关误差等滤波方法分别应用于合成的质量变化趋势数字模型,检测不同滤波方法消除条带噪声的能力及其对真实信号的影响.滤波结果显示,与目前常用的高斯滤波和去相关误差滤波器相比,本文滤波方法在有效移除条带噪声的同时,具有有效信号幅度衰减小、有效信号形变小以及保存了更多的短波长细节信息等优势;此外,统计结果显示,本文滤波结果在信号最大值、最小值以及残差均方根等方面均与模拟真实信号最为接近.相比300km高斯平滑和组合滤波结果,有效信号振幅的极小值和极大值分别提高了约18%和6%,残差均方根分别降低了25%和33%.说明本文滤波方法移除GRACE相关误差的同时,在保留有效信号方面具有明显的优势.

英文摘要：

The errors, which are present in short wavelength components of time-variable gravity solutions produced by the Gravity Recovery and Climate Experiment （GRACE） satellite, manifest themselves in maps of gravity change as long, linear features generally trending in north- south （i. e. , stripes）. Thus, filtering is needed to remove them before using the GRACE time- variable gravity solutions. Here, we introduce a smoothness priors method to remove such stripe errors with application to a synthetic mass change model and show significant effects. The method used in this paper is based on the smoothness priors approach and operates like a time-varying finite impulse response high-pass filter. In this paper, the correlative errors in maps of GRACE mass variation are regard as the high frequency signals varying with longitude reserved when the data is filtered, then we subtract them from the original data to get the filtering result. In order to examine the effect of the filter, we generate a model consisting of two parts： ‘true’ geophysical signals and stripe noise. First, following the approaches of Duan et al. , we construct a numerical model of mass change trend composed of the NOAH GLDAS model between the latitudes of ±60°, a surface mass change rate corresponding to the gravity variation of a PGR model based on the RF3L20 ice model over Canada and the GRACE mass variation trend over the Arctic and Antarctic areas （90°S--60°S, 60°N--90°N）. We use this numerical model as the ‘true’ geophysical signal of mass change. In addition, we also need a north-south ‘stripe model similar to that in GRACE data to test the effect of the filter and its signal distortion. The stripe model is constructed using the difference between the GRACE monthly time-variable gravity solutions and the filtered field. Its amplitude is reduced to be comparable with that of the numerical mass change trend model. The filtered field is computed using the algorithms of Swenson and Wahr and an isotropic Gaussian filte