We propose a benchmark for comparing gap-filling techniques used on global time-variable gravity field time-series. The Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On missions provide products to study the Earth’s time-variable gravity field. However, the presence of missing months in the measurements poses challenges for understanding specific Earth processes through the gravity field. We reproduce, adapt, and compare satellite-monitoring and interpolation techniques for filling these missing months in GRACE and GRACE Follow-On products on a global scale. Satellite-monitoring techniques utilize solutions from Swarm and satellite laser ranging, while interpolation techniques rely on GRACE and/or Swarm solutions. We assess a wide range of interpolation techniques, including least-squares fitting, principal component analysis, singular spectrum analysis, multichannel singular spectrum analysis, auto-regressive models, and the incorporation of prior data in these techniques. To inter-compare these techniques, we employ a remove-and-restore approach, removing existing GRACE products and predicting missing months using interpolation techniques. We provide detailed comparisons of the techniques and discuss their strengths and limitations. The auto-regressive interpolation technique delivers the best score according to our evaluation metric. The interpolation based on a least-squares fitting of constant, trend, annual, and semi-annual cycles offers a simple and effective prediction with a good score. Through this assessment, we establish a starting benchmark for gap-filling techniques in Earth’s time-variable gravity field analysis.

我们提出了一个基准，用于比较全局时间可变重力场时间序列上使用的间隙填充技术。重力恢复和气候实验 （GRACE） 和 GRACE 后续任务为研究地球的时间可变重力场提供了产品。然而，测量中存在缺失的月份，这为通过重力场理解特定的地球过程带来了挑战。我们在全球范围内复制、改编和比较卫星监测和插值技术，以填补 GRACE 和 GRACE Follow-On 产品中缺失的月份。卫星监测技术利用 Swarm 和卫星激光测距的解决方案，而插值技术则依赖于 GRACE 和/或 Swarm 解决方案。我们评估了广泛的插值技术，包括最小二乘拟合、主成分分析、奇异谱分析、多通道奇异谱分析、自回归模型以及在这些技术中纳入先验数据。为了相互比较这些技术，我们采用了删除和恢复方法，删除现有的 GRACE 产品并使用插值技术预测缺失月份。我们提供了这些技术的详细比较，并讨论了它们的优点和局限性。自回归插值技术根据我们的评估指标提供最佳分数。基于常数、趋势、年度和半年周期的最小二乘拟合的插值提供了简单有效的预测，并具有良好的分数。通过这项评估，我们为地球时变重力场分析中的间隙填充技术建立了一个起始基准。