GGM02 – An improved Earth gravity field model from GRACE

A new generation of Earth gravity field models called GGM02 are derived using approximately 14 months of data spanning from April 2002 to December 2003 from the Gravity Recovery And Climate Experiment (GRACE). Relative to the preceding generation, GGM01, there have been improvements to the data products, the gravity estimation methods and the background models. Based on the calibrated covariances, GGM02 (both the GRACE-only model GGM02S and the combination model GGM02C) represents an improvement greater than a factor of two over the previous GGM01 models. Error estimates indicate a cumulative error less than 1 cm geoid height to spherical harmonic degree 70, which can be said to have met the GRACE minimum mission goals. Electronic Supplementary Material Supplementary material is available in the online version of this article at     

Combination of GRACE monthly gravity field solutions from different processing strategies

We combine the publicly available GRACE monthly gravity field time series to produce gravity fields with reduced systematic errors. We first compare the monthly gravity fields in the spatial domain in terms of signal and noise. Then, we combine the individual gravity fields with comparable signal content, but diverse noise characteristics. We test five different weighting schemes: equal weights, non-iterative coefficient-wise, order-wise, or field-wise weights, and iterative field-wise weights applying variance component estimation (VCE). The combined solutions are evaluated in terms of signal and noise in the spectral and spatial domains. Compared to the individual contributions, they in general show lower noise. In case the noise characteristics of the individual solutions differ significantly, the weighted means are less noisy, compared to the arithmetic mean: The non-seasonal variability over the oceans is reduced by up to 7.7% and the root mean square (RMS) of the residuals of mass change estimates within Antarctic drainage basins is reduced by 18.1% on average. The field-wise weighting schemes in general show better performance, compared to the order- or coefficient-wise weighting schemes. The combination of the full set of considered time series results in lower noise levels, compared to the combination of a subset consisting of the official GRACE Science Data System gravity fields only: The RMS of coefficient-wise anomalies is smaller by up to 22.4% and the non-seasonal variability over the oceans by 25.4%. This study was performed in the frame of the European Gravity Service for Improved Emergency Management (EGSIEM; http://www.egsiem.eu) project. The gravity fields provided by the EGSIEM scientific combination service (ftp://ftp.aiub.unibe.ch/EGSIEM/) are combined, based on the weights derived by VCE as described in this article.     

On the postprocessing removal of correlated errors in GRACE temporal gravity field solutions

We revisit the empirical moving window filtering method of Swenson and Wahr (Geophys Res Lett 33:L08402, 2006) and its variants, Chambers (Geophys Res Lett 33:L17603, 2006) and Chen et al. (Geophys Res Lett 34: L13302, 2007), for reducing the correlated errors in the Stokes coefficients (SCs) of the spherical harmonic expansion of the GRACE determined monthly geopotential solutions. Based on a comparison of the three published approaches mentioned, we propose a refined approach for choosing parameters of the decorrelation filter. Our approach is based on the error pattern of the SCs in the monthly GRACE geopotential solutions. We keep a portion of the lower degree-order SCs with the smallest errors unchanged, and high-pass filter the rest using a moving window technique, with window width decreasing as the error of the SCs increases. Both the unchanged portion of SCs and the window width conform with the error pattern, and are adjustable with a parameter. Compared to the three published approaches mentioned, our unchanged portion of SCs and window width depend on both degree and order in a more complex way. We have used the trend of mass change to test various parameters toward a preferred choice for a global compromise between the removal of the correlated errors and the minimization of signal distortion.     

Reducing errors in the GRACE gravity solutions using regularization

The nature of the gravity field inverse problem amplifies the noise in the GRACE data, which creeps into the mid and high degree and order harmonic coefficients of the Earth’s monthly gravity fields provided by GRACE. Due to the use of imperfect background models and data noise, these errors are manifested as north-south striping in the monthly global maps of equivalent water heights. In order to reduce these errors, this study investigates the use of the L-curve method with Tikhonov regularization. L-curve is a popular aid for determining a suitable value of the regularization parameter when solving linear discrete ill-posed problems using Tikhonov regularization. However, the computational effort required to determine the L-curve is prohibitively high for a large-scale problem like GRACE. This study implements a parameter-choice method, using Lanczos bidiagonalization which is a computationally inexpensive approximation to L-curve. Lanczos bidiagonalization is implemented with orthogonal transformation in a parallel computing environment and projects a large estimation problem on a problem of the size of about 2 orders of magnitude smaller for computing the regularization parameter. Errors in the GRACE solution time series have certain characteristics that vary depending on the ground track coverage of the solutions. These errors increase with increasing degree and order. In addition, certain resonant and near-resonant harmonic coefficients have higher errors as compared with the other coefficients. Using the knowledge of these characteristics, this study designs a regularization matrix that provides a constraint on the geopotential coefficients as a function of its degree and order. This regularization matrix is then used to compute the appropriate regularization parameter for each monthly solution. A 7-year time-series of the candidate regularized solutions (Mar 2003–Feb 2010) show markedly reduced error stripes compared with the unconstrained GRACE release 4 solutions (RL04) from the Center for Space Research (CSR). Post-fit residual analysis shows that the regularized solutions fit the data to within the noise level of GRACE. A time series of filtered hydrological model is used to confirm that signal attenuation for basins in the Total Runoff Integrating Pathways (TRIP) database over 320 km radii is less than 1 cm equivalent water height RMS, which is within the noise level of GRACE.     

插值法恢复CHAMP卫星姿态间断数据对惯性系非保守力加速度的影响

本文主要讨论由GFZ数据中心提供的加速度数据中姿态数据间断的处理方法。对不连续的15天的姿态数据的间断情况做了统计,并且对姿态数据的日变化性质做了分析,提出了姿态数据变化呈周期性曲线变化的特点·对姿态数据进行线性插值并与正常数据作了比较,得到一些有益的的结论。     

A parametric study on the impact of satellite attitude errors on GOCE gravity field recovery

In the recent design of the Gravity field and steady-state Ocean Circulation Explorer (GOCE) satellite mission, the gravity gradients are defined in the gradiometer reference frame (GRF), which deviates from the actual flight direction (local orbit reference frame, LORF) by up to 3–4°. The main objective of this paper is to investigate the effect of uncertainties in the knowledge of the gradiometer orientation due to attitude reconstitution errors on the gravity field solution. In the framework of several numerical simulations, which are based on a realistic mission configuration, different scenarios are investigated, to provide the accuracy requirements of the orientation information. It turns out that orientation errors have to be seriously considered, because they may represent a significant error component of the gravity field solution. While in a realistic mission scenario (colored gradiometer noise) the gravity field solutions are quite insensitive to small orientation biases, random noise applied to the attitude information can have a considerable impact on the accuracy of the resolved gravity field models.     

GRACE和GOCE卫星监测的青藏高原重力场特征

文章阐述了对青藏高原重力场进行研究的意义,并进一步利用重力卫星GRACE和GOCE的数据对该区域的重力场特征进行了描述.通过对该区域的重力异常、径向引力梯度的计算和分析,可以得出:在青藏高原的西部,有明显的3条重力异常区,这与当地的地形有关,也与断层的位置有关;引力梯度比重力异常具有更高的空间分辨率;重力变化剧烈的区域与梯度的异常区有一定的对应关系,同时也是地球动力活动变化剧烈的区域.     

融合多源重力数据构建局部高阶重力场模型

针对利用带限型径向基函数,融合航空和地面重力数据构建高阶地球重力场模型时,航空、地面重力数据的频谱信息不一致问题,该文提出残差与先验精度比较分析法,确定出了研究区域航空数据的最佳频谱范围.结果 表明:航空重力数据系统性偏差及其径向基函数展开阶次不当可分别导致±0.019m和±0.007 m的建模误差.基于此,构建了局部区域6000阶的径向基函数融合地球重力场模型CBFM2020.通过与GPS/水准数据比较,CBFM2020的精度比EGM2008、USGG2012以及仅由地面数据得到的重力场模型均有所提高.模型建立过程中航空重力数据未进行向下延拓,而是直接在原始高度与地面数据进行融合,可为局部地区高阶重力场模型的构建提供一定参考.     

An improved method of joint inversion using GPS and gravity observation data

On the basis of the dislocation theory and gravity observation, a joint inversion model is presented with a fitting factor A scaling amplitudes between the gravity and GPS observation data. The test results show that the new joint model is better than that taking the scale factor ）, as a constant from the inversion result of MSE （mean square error）. In addition, the random cost method used in the inversion algorithm is revised and improved, which shows that the improved random cost method can easily get the local minimum value and greatly decrease the iteration steps.     

Swarm卫星精密定轨与加速度法恢复地球重力场

CHAMP卫星和GOCE卫星分别于2010年和2013年坠落,GRACE卫星仍然超预期运行,随时会坠落,而后续重力卫星计划GRACE Follow-On预计2018年发射,在此期间,Swarm作为唯一的低轨重力观测卫星,将填补重力卫星观测的空白。本文对Swarm卫星精密定轨与加速度法恢复地球重力场进行了研究,实现了Swarm卫星运动学厘米级精密定轨和简化动力学厘米级精密定轨,并基于加速度法恢复了Swarm地球重力场模型。     

A simulation study of the errors of omission and commission for GRACE RL01 gravity fields

This paper examines the influence that certain omission and commission errors can have on the gravity field models estimated from the initial release of data (RL01) from the Gravity Recovery And Recovery Experiment (GRACE) satellite mission. The effects of omission errors were analyzed by limiting the degree and order to which the GPS and K-band range-rate (KBR) measurement partials were extended in the solution process. The commission error studies focused on the impact of an imperfect mean reference gravity field model on the solution. Combinations of both of these error sources were also explored. The nature of these errors makes them difficult to distinguish from the true gravity signal, so the exploration of these error sources was performed using simulations; however, comparisons to real-data solutions are provided. The results show how each of the specific error sources investigated influences the gravity field solution. The simulations also show how all of the errors examined can be sufficiently mitigated through the appropriate choice of processing parameters.     

GRACE时变重力场的滤波理论及质量变化分析

正由于GRACE(gravity recovery and climate experiment)时变重力场模型直接解算质量变化时存在较大的误差,需要对其进行相应的滤波处理,提高质量变化的反演精度。本文总结了GRACE监测全球与区域质量变化的研究进展,分析了最新时变重力场模型的精度及其滤波方法;提出了基于重力位系数协方差阵的时变重力场滤波方法;分析了南极冰盖的质量变化、亚马孙流域陆地水质量和海平面变化。本文的研究成果及创新点主要包括以下几个方面:     

改进的能量守恒方法及其在CHAMP重力场恢复中的应用

An efficient method for gravity field determination from CHAMP orbits and accelerometer data is referred to as the energy balance approach. A new CHAMP gravity field recovery strategy based on the improved energy balance approach IS developed in this paper. The method simultaneously solves the spherical harmonic coefficients, daily Integration constant, scale and bias parameters. Two 60 degree and order gravitational potential models, XISM-CHAMPO1S from the classical energy balance approach, and XISM-CHAMPO2S from the improved energy balance, are determined using about one year＇s worth of CHAMP kinematic orbits from TUM and accelerometer data from GFZ. Comparisons among XISM-CHAMPO1S, XISM-CHAMPO2S, EIGEN-CGO3C, EIGEN-CHAMPO3S, EIGEN2, ENIGNIS and EGM96 are made. The results show that the XISM-CHAMPO2S model is more accurate than EGM96, EIGENIS, EIGEN2 and XISM-CHAMPO1S at the same degree and order, and has almost the same accuracy as EIGEN-CHAMPO3S.     

基于ICESat-GLAS和GRACE卫星遥感资料的高亚洲冰川物质平衡变化研究

针对高亚洲冰川物质平衡和水资源的热点问题,首先利用2003—2009年ICESat-GLAS和2000年2月SRTM DEM V2.1资料,推算高亚洲地区冰川表面高程和冰量变化;其次,利用GRACE卫星的2002年8月至2012年12月时变重力场资料反演出研究区地表质量变化,继而在综合分析各流域内地表各种物质变化的基础上,恢复出流域冰川物质平衡;再次,根据高亚洲流域冰川的物质平衡状况,估算了外流河流域冰川物质亏损对海平面上升和内流河流域冰川物质亏损对内陆湖泊水量的贡献。     

基于SLR的GRACE卫星定轨中重力场模型对轨道精度的影响

以GRACE卫星为例,分析比较利用SLR观测资料进行卫星定轨时,采用不同重力场模型对GRACE卫星定轨精度的影响;以及重力场截断阶引起的积分轨道差异;同时,将定轨结果与采用GPS确定的定轨结果进行比较,分析与GPS定轨结果的差异.实验证明.重力场模型选择GGM02C的定轨结果优于选择JGM-3的定轨结果,基于SLR的定...     

Understanding data noise in gravity field recovery on the basis of inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE

Spectral analysis of data noise is performed in the context of gravity field recovery from inter-satellite ranging measurements acquired by the satellite gravimetry mission GRACE. The motivation of the study is two-fold: (i) to promote a further improvement of GRACE data processing techniques and (ii) to assist designing GRACE follow-on missions. The analyzed noise realizations are produced as the difference between the actual GRACE inter-satellite range measurements and the predictions based on state-of-the-art force models. The exploited functional model is based on the so-called “range combinations,” which can be understood as a finite-difference analog of inter-satellite accelerations projected onto the line-of-sight connecting the satellites. It is shown that low-frequency noise is caused by limited accuracy of the computed GRACE orbits. In the first instance, it leads to an inaccurate estimation of the radial component of the inter-satellite velocities. A large impact of this component stems from the fact that it is directly related to centrifugal accelerations, which have to be taken into account when the measured range-accelerations are linked with inter-satellite accelerations. Another effect of orbit inaccuracies is a miscalculation of forces acting on the satellites (particularly, the one described by the zero-degree term of the Earth’s gravitational field). The major contributors to the noise budget at high frequencies (above 9?mHz) are (i) ranging sensor errors and (ii) limited knowledge of the Earth’s static gravity field at high degrees. Importantly, we show that updating the model of the static field on the basis of the available data must be performed with a caution as the result may not be physical due to a non-unique recovery of high-degree coefficients. The source of noise in the range of intermediate frequencies (1–9?mHz), which is particularly critical for an accurate gravity field recovery, is not fully understood yet. We show, however, that it cannot be explained by inaccuracies in background models of time-varying gravity field. It is stressed that most of the obtained results can be treated as sufficiently general (i.e., applicable in the context of a statistically optimal estimation based on any functional model).     

An estimate of the errors in gravity ocean tide loading computations

The error contributions within the ocean tide loading (OTL) convolution integral computation were determined to be able to estimate the numerical accuracy of the gravity OTL values. First, the comparison of four OTL programs by different authors (CONMODB, GOTIC2, NLOADF and OLFG/OLMPP) at ten globally distributed gravity stations using exactly the same input values shows discrepancies between 2% and 5%. A new program, called CARGA, was written that is able to reproduce the results of these programs to a level of 0.1%. This has given us the ability to state with certainty the cause of the discrepancies among the four programs. It is shown that by choosing an appropriate interpolation of the Green’s function, refinement of the integration mesh and a high-resolution coastline, an accuracy level of better than 1% can be obtained for stations in Europe. Besides this numerical accuracy, there are errors in the ocean tide model such as a 1% uncertainty in the mean value of the sea-water density and the lack of conservation of tidal water mass, which can produce offsets of around 0.04 μgal.     

基于GRACE时变重力场模型研究印度河-恒河流域水储量变化

印度河和恒河是亚洲的重要河流,流经区域也是印度次大陆人口最集中的地区,研究该区域水储量变化对于保障当地生态环境和经济发展有重要意义.本文利用GRACE_RL05时变重力场模型反演2002-2016年的印度河-恒河流域内陆地水储量变化,并结合GLDAS水文模型获取区域地下水储量变化.研究发现:2002-2016年印度河和恒河流域陆地水储量和地下水储量均呈下降趋势,其中两流域的地下水下降的速率(分别为-7.2 mm／a和-4.7 mm／a)超过整体水储量的下降趋势;综合水文模型数据分析表明:该区域水储量下降的原因是蒸发量上升和降水持续减少的背景下,农业灌溉过程中过度抽采地下水导致的.