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首页> 外文期刊>Journal of geodynamics >Estimation and reduction of random noise in mass anomaly time-series from satellite gravity data by minimization of month-to-month year-to-year double differences
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Estimation and reduction of random noise in mass anomaly time-series from satellite gravity data by minimization of month-to-month year-to-year double differences

机译：通过最小化逐月逐年双倍差异，从卫星重力数据估算和减少质量异常时间序列中的随机噪声

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摘要

We propose a technique to regularize a GRACE-based mass-anomaly time-series in order to (i) quantify the Standard Deviation (SD) of random noise in the data, and (ii) reduce the level of that noise. The proposed regularization functional minimizes the Month-to-month Year-to-year Double Differences (MYDD) of mass anomalies. As such, it does not introduce any bias in the linear trend and the annual component, two of the most common features in GRACE-based mass anomaly time-series. In the context of hydrological and ice sheet studies, the proposed regularization functional can be interpreted as an assumption about the stationarity of climatological conditions. The optimal regularization parameter and noise SD are obtained using Variance Component Estimation. To demonstrate the performance of the proposed technique, we apply it to both synthetic and real data. In the latter case, two geographic areas are considered: the Tonle Sap basin in Cambodia and Greenland. We show that random noise in the data can be efficiently (1.5-2 times) mitigated in this way, whereas no noticeable bias is introduced. We also discuss various findings that can be made on the basis of the estimated noise SD. We show, among others, that knowledge of noise SD facilitates the analysis of differences between GRACE-based and alternative estimates of mass variations. Moreover, inaccuracies in the latter can also be quantified in this way. For instance, we find that noise in the surface mass anomalies in Greenland estimated using the Regional Climate Model RACMO2.3 is at the level of 2-6 cm equivalent water heights. Furthermore, we find that this noise shows a clear correlation with the amplitude of annual mass variations: it is lowest in the north-west of Greenland and largest in the south. We attribute this noise to limitations in the modelling of the meltwater accumulation and run-off.
机译：我们提出一种技术来规范化基于GRACE的质量异常时间序列，以便（i）量化数据中随机噪声的标准差（SD），以及（ii）降低该噪声的水平。拟议的正则化函数将质量异常的月度与年度的逐年双倍差异（MYDD）最小化。这样，它不会在线性趋势和年度分量（基于GRACE的质量异常时间序列中最常见的两个特征）中引入任何偏差。在水文和冰盖研究的背景下，提出的正则化函数可以解释为关于气候条件平稳性的假设。使用方差分量估计获得最佳正则化参数和噪声SD。为了演示所提出技术的性能，我们将其应用于合成数据和真实数据。在后一种情况下，考虑了两个地理区域：柬埔寨的洞里萨湖盆地和格陵兰。我们显示，数据中的随机噪声可以通过这种方式有效地缓解（1.5-2倍），而没有引入明显的偏差。我们还将讨论可以基于估计的噪声SD得出的各种发现。我们表明，除其他外，噪声SD的知识有助于分析基于GRACE的质量变化和替代估计的质量之间的差异。此外，后者的误差也可以通过这种方式进行量化。例如，我们发现使用区域气候模型RACMO2.3估算的格陵兰岛表面质量异常的噪声处于2-6 cm当量水高的水平。此外，我们发现，这种噪声与年质量变化的幅度具有明显的相关性：在格陵兰西北部最低，在南部最高。我们将此噪声归因于融水积聚和径流建模的局限性。

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