With the successful completion of European Space Agency (ESA)’s PolarGAP campaign, ground gravity data are now available for both polar regions. Therefore, it is now possible to solve the GOCE polar gap problem in satellite-only gravity field recovery by using additional polar ground gravity data instead of some regularization methods. However, ground gravimetry data need to be filtered to remove the short-wavelength information beyond a certain harmonic degree to avoid spectral leakage when inferring satellite-only gravity field models. For the Arctic, the ArcGP data set was successfully applied when inferring the high-resolution gravity field model EGM2008 which could be used for this filtering there. For Antarctica, a combination of latest airborne gravimetry data from ESA’s PolarGap campaign and some previous gravity data was recently published which was irregularly distributed in space and still had some small gaps within the GOCE south polar gap. Therefore, we proposed a point mass modeling method for this filtering which was similar to the way using EGM2008 for such filtering to the ground gravity data in the Arctic. Furthermore, a variance component estimation was applied to combine the normal equations from the different sources to build a global gravity field model called IGGT_R1C. Then, this model’s accuracy was evaluated by comparison with other gravity field models in terms of difference degree amplitudes, gravity anomaly differences as well as external checking by obit adjustment and gravity data in the GOCE polar gap areas. This gravity field model performed well globally according to these checking results; especially, the RMS of the residuals between the filtered gravity data and that calculated from IGGT_R1C was the smallest (2.6 mGal in the Arctic and 5.4 mGal in Antarctica) compared with that of the relevant satellite-only gravity field models, e.g., GOCO05s. Therefore, the disturbing impact of the GOCE polar data gap problem could be solved by adding the polar ground gravity data.

中文翻译：

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利用真实极地重力数据解决单卫星重力场恢复中的GOCE极地间隙问题

随着欧洲航天局 (ESA) 的 PolarGAP 活动的成功完成，两个极地地区的地面重力数据现已可用。因此，现在可以通过使用额外的极地地面重力数据代替一些正则化方法来解决仅卫星重力场恢复中的 GOCE 极地间隙问题。然而，在推断纯卫星重力场模型时，需要对地面重力数据进行过滤以去除超出一定谐波程度的短波长信息，以避免光谱泄漏。对于北极，ArcGP 数据集在推断高分辨率重力场模型EGM2008 时成功应用，该模型可用于该处的过滤。对于南极洲，来自 ESA 的 PolarGap 运动的最新航空重力测量数据和最近公布的一些先前重力数据的组合，这些数据在太空中不规则分布，并且在 GOCE 南极间隙内仍然存在一些小间隙。因此，我们提出了一种类似于使用EGM2008对北极地面重力数据进行这种过滤的方式的点质量建模方法来进行这种过滤。此外，应用方差分量估计来组合来自不同来源的法线方程，以构建称为 IGGT_R1C 的全球重力场模型。然后，通过与其他重力场模型的差度幅值、重力异常差异以及轨道校正外部校验和GOCE极隙区重力数据等方面的对比，对该模型的精度进行了评价。根据这些检查结果，该重力场模型在全球范围内表现良好；特别是过滤后的重力数据与IGGT_R1C计算的残差RMS与相关的仅卫星重力场模型（例如GOCO05s）相比，最小（北极为2.6 mGal，南极为5.4 mGal）。因此，可以通过添加极地重力数据来解决GOCE极地数据缺口问题的干扰影响。