For further improvements of gravity field models based on Gravity Recovery and Climate Experiment (GRACE) observations, it is necessary to identify the error sources within the recovery process. Observation residuals obtained during the gravity field recovery contain most of the measurement and modeling errors and thus can be considered a realization of actual errors. In this work, we investigate the ability of wavelets to help in identifying specific error sources in GRACE range-rate residuals. The multiresolution analysis (MRA) using discrete wavelet transform (DWT) is applied to decompose the residual signal into different scales with corresponding frequency bands. Temporal, spatial, and orbit-related features of each scale are then extracted for further investigations. The wavelet analysis has proven to be a practical tool to find the main error contributors. Besides the previously known sources such as K-band ranging (KBR) system noise and systematic attitude variations, this method clearly shows effects which the classic spectral analysis is hardly able or unable to represent. These effects include long-term signatures due to satellite eclipse crossings and dominant ocean tide errors.

中文翻译：

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多分辨率小波分析应用于GRACE范围率残差

为了进一步改善基于重力恢复和气候实验（GRACE）观测值的重力场模型，有必要在恢复过程中确定误差源。在重力场恢复过程中获得的观测残差包含大多数测量和建模误差，因此可以认为是实际误差的实现。在这项工作中，我们研究了小波帮助识别GRACE范围率残差中特定误差源的能力。应用离散小波变换（DWT）的多分辨率分析（MRA）将残差信号分解为具有相应频带的不同比例。然后提取每个比例尺的时间，空间和与轨道相关的特征，以供进一步研究。小波分析已被证明是找到主要误差因素的实用工具。除了先前已知的来源（例如K波段测距（KBR）系统噪声和系统姿态变化）之外，该方法还清楚地显示了经典频谱分析难以或无法代表的效果。这些影响包括由于卫星月食穿越和主要的海洋潮汐误差引起的长期签名。