Gravity Recovery And Climate Experiment Data Processing Research Articles

A New GRACE Filtering Approach Based on Iterative Image Convolution

AbstractThe Gravity Recovery and Climate Experiment (GRACE) mission and its successor GRACE Follow‐On (GRACE‐FO) mission provide humans with a unique perspective on mass distribution through accurate temporal gravity field models. However, these models suffer from unavoidable stripe errors in the spatial domain. A new approach based on image processing has been developed for filtering GRACE and GRACE‐FO spherical harmonic solutions. In this approach, the gravity fields are treated as noise‐contaminated images, and the stripe errors are expected to be suppressed by convolving the input with an appropriate kernel. To verify the effectiveness of the proposed approach, a closed‐loop simulation environment over the nominal mission lifetime of 5 years is designed. Image processing indicators and geophysical information evaluation results demonstrate the superior performance of the new approach in suppressing stripe errors. In the context of real GRACE data processing, our new approach still yields better results as follows: (a) The comprehensive ability of de‐striping and retaining details has slight advantages over some commonly used filters, as demonstrated through spatial distributions, amplitude spectra, and coefficient spectra. (b) The equivalent water height time series at the basin scale is comparable to the results derived from other filters or Level‐3 products. (c) The estimated signal amplitude of the Mw9.1 2004 Sumatra earthquake co‐seismic gravity change is 52.7% larger than that derived from the Gaussian 300 km filter, which is much closer to the theoretical value derived from the dislocation theory.

Mutual Consistency of Groundwater Storage Changes Derived From GRACE and From Baseflow Recessions in the Central Yangtze River Basin

AbstractA comparison is presented between groundwater storage trends derived from Gravity Recovery and Climate Experiment (GRACE) satellite measurements and trends derived from unregulated river baseflow observations in 19 catchments in tributaries of the Central Yangtze River. The two sets of trends are well correlated with a coefficient of r = 0.64, but the baseflow‐derived trends were found to be on average about 33% smaller than those based on the GRACE measurements. Although other factors undoubtedly play a role, the difference in trend magnitudes of the two sets of trends is concluded to be mainly due (i) to remaining uncertainties and complexities in GRACE data processing and (ii) to the likely gradual shrinking of the contributing catchment areas during the baseflow recession process. The present findings should be useful in extending GRACE measurements to longer periods on the basis of long‐term streamflow measurements and in recovering missing data.

Prograde and Retrograde Terms of Gravimetric Polar Motion Excitation Estimates from the GRACE Monthly Gravity Field Models

From 2002 to 2017, the Gravity Recovery and Climate Experiment (GRACE) mission’s twin satellites measured variations in the mass redistribution of Earth’s superficial fluids, which disturb polar motion (PM). In this study, the PM excitation estimates were computed from two recent releases of GRACE monthly gravity field models, RL05 and RL06, and converted into prograde and retrograde circular terms by applying the complex Fourier transform. This is the first such analysis of circular parts in GRACE-based excitations. The obtained series were validated by comparison with the residuals of observed polar motion excitation (geodetic angular momentum (GAM)–atmospheric angular momentum (AAM)–oceanic angular momentum (OAM) (GAO)) determined from precise geodetic measurements of the pole coordinates. We examined temporal variations of hydrological excitation function series (or hydrological angular momentum, HAM) in four spectral bands: seasonal, non-seasonal, non-seasonal short-term, and non-seasonal long-term. The general conclusions arising from the conducted analyses of prograde and retrograde terms were consistent with the findings from the equatorial components of PM excitation studies drawn in previous research. In particular, we showed that the new GRACE RL06 data increased the consistency between different solutions and improved the agreement between GRACE-based excitation series and reference data. The level of agreement between HAM and GAO was dependent on the oscillation considered and was higher for long-term than short-term variations. For most of the oscillations considered, the highest agreement with GAO was obtained for CSR RL06 and ITSG-Grace2018 solutions. This study revealed that both prograde and retrograde circular terms of PM excitation can be determined by GRACE with similar levels of accuracy. The findings from this study may help in choosing the most appropriate GRACE solution for PM investigations and can be useful in future improvements to GRACE data processing.

Sea-level fingerprints emergent from GRACE mission data

Abstract. The Gravity Recovery and Climate Experiment (GRACE) mission data have an important, if not revolutionary, impact on how scientists quantify the water transport on the Earth's surface. The transport phenomena include land hydrology, physical oceanography, atmospheric moisture flux, and global cryospheric mass balance. The mass transport observed by the satellite system also includes solid Earth motions caused by, for example, great subduction zone earthquakes and glacial isostatic adjustment (GIA) processes. When coupled with altimetry, these space gravimetry data provide a powerful framework for studying climate-related changes on decadal timescales, such as ice mass loss and sea-level rise. As the changes in the latter are significant over the past two decades, there is a concomitant self-attraction and loading phenomenon generating ancillary changes in gravity, sea surface, and solid Earth deformation. These generate a finite signal in GRACE and ocean altimetry, and it may often be desirable to isolate and remove them for the purpose of understanding, for example, ocean circulation changes and post-seismic viscoelastic mantle flow, or GIA, occurring beneath the seafloor. Here we perform a systematic calculation of sea-level fingerprints of on-land water mass changes using monthly Release-06 GRACE Level-2 Stokes coefficients for the span April 2002 to August 2016, which result in a set of solutions for the time-varying geoid, sea-surface height, and vertical bedrock motion. We provide both spherical harmonic coefficients and spatial maps of these global field variables and uncertainties therein (https://doi.org/10.7910/DVN/8UC8IR; Adhikari et al., 2019). Solutions are provided for three official GRACE data processing centers, namely the University of Texas Austin's Center for Space Research (CSR), GeoForschungsZentrum Potsdam (GFZ), and Jet Propulsion Laboratory (JPL), with and without rotational feedback included and in both the center-of-mass and center-of-figure reference frames. These data may be applied for either study of the fields themselves or as fundamental filter components for the analysis of ocean-circulation- and earthquake-related fields or for improving ocean tide models.

Tide gauge records reveal improved processing of gravity recovery and climate experiment time-variable mass solutions over the coastal ocean

Monthly ocean bottom pressure solutions from the Gravity Recovery and Climate Experiment (GRACE), derived using surface spherical cap mass concentration (MC) blocks and spherical harmonics (SH) basis functions, are compared to tide gauge (TG) monthly averaged sea level data over 2003–2015 to evaluate improved gravimetric data processing methods near the coast. MC solutions can explain ≳ 42 per cent of the monthly variance in TG time-series over broad shelf regions and in semi-enclosed marginal seas. MC solutions also generally explain ~ 5–32 per cent more TG data variance than SH estimates. Applying a coastline resolution improvement algorithm in the GRACE data processing leads to ~ 31 per cent more variance in TG records explained by the MC solution on average compared to not using this algorithm. Synthetic observations sampled from an ocean general circulation model exhibit similar patterns of correspondence between modelled TG and MC time-series and differences between MC and SH time-series in terms of their relationship with TG time-series, suggesting that observational results here are generally consistent with expectations from ocean dynamics. This work demonstrates the improved quality of recent MC solutions compared to earlier SH estimates over the coastal ocean, and suggests that the MC solutions could be a useful tool for understanding contemporary coastal sea level variability and change.

The unexpected signal in GRACE estimates of $$C_{20}$$

For science applications of the gravity recovery and climate experiment (GRACE) monthly solutions, the GRACE estimates of \(C_{20}\) (or \(J_{2}\)) are typically replaced by the value determined from satellite laser ranging (SLR) due to an unexpectedly strong, clearly non-geophysical, variation at a period of \(\sim \)160 days. This signal has sometimes been referred to as a tide-like variation since the period is close to the perturbation period on the GRACE orbits due to the spherical harmonic coefficient pair \(C_{22}/S_{22}\) of S2 ocean tide. Errors in the S2 tide model used in GRACE data processing could produce a significant perturbation to the GRACE orbits, but it cannot contribute to the \(\sim \)160-day signal appearing in \(C_{20}\). Since the dominant contribution to the GRACE estimate of \(C_{20}\) is from the global positioning system tracking data, a time series of 138 monthly solutions up to degree and order 10 (\(10\times 10\)) were derived along with estimates of ocean tide parameters up to degree 6 for eight major tides. The results show that the \(\sim \)160-day signal remains in the \(C_{20}\) time series. Consequently, the anomalous signal in GRACE \(C_{20}\) cannot be attributed to aliasing from the errors in the S2 tide. A preliminary analysis of the cross-track forces acting on GRACE and the cross-track component of the accelerometer data suggests that a temperature-dependent systematic error in the accelerometer data could be a cause. Because a wide variety of science applications relies on the replacement values for \(C_{20}\), it is essential that the SLR estimates are as reliable as possible. An ongoing concern has been the influence of higher degree even zonal terms on the SLR estimates of \(C_{20}\), since only \(C_{20}\) and \(C_{40}\) are currently estimated. To investigate whether a better separation between \(C_{20}\) and the higher-degree terms could be achieved, several combinations of additional SLR satellites were investigated. In addition, a series of monthly gravity field solutions (\(60\times 60\)) were estimated from a combination of GRACE and SLR data. The results indicate that the combination of GRACE and SLR data might benefit the resonant orders in the GRACE-derived gravity fields, but it appears to degrade the recovery of the \(C_{20}\) variations. In fact, the results suggest that the poorer recovery of \(C_{40}\) by GRACE, where the annual variation is significantly underestimated, may be affecting the estimates of \(C_{20}\). Consequently, it appears appropriate to continue using the SLR-based estimates of \(C_{20}\), and possibly also \(C_{40}\), to augment the existing GRACE mission.

Seasonal crustal vertical deformation induced by environmental mass loading in mainland China derived from GPS, GRACE and surface loading models

Obvious seasonal crustal vertical deformation largely related to mass redistribution on the Earth’s surface can be captured by Gravity Recovery and Climate Experiment (GRACE), simulated by surface loading models (SLMs), and recorded by continuous Global Positioning System (GPS). Vertical deformation time series at 224 GPS stations with more than four-year continuous observations are compared with time series obtained by GRACE and SLMs with the aim of investigating the consistency of the seasonal crustal vertical deformation obtained by different techniques in mainland China. Results of these techniques show obvious seasonal vertical deformation with high consistency at almost all stations. The GPS-derived seasonal vertical deformation can be explained, to some content, by the environmental mass redistribution effect represented by GRACE and SLMs. Though the mean weighted root mean square reduction is 34% after removing the environmental mass loading from the monthly GPS height time series (up to 47% for the mean annual signals), systematic signals are still evident in the residual time series. The systematic residuals are probably attributed to GPS related errors, such as draconitic errors, while the leakage errors in the GRACE data processing and unmodeled components in land water storage should be considered in some regions. Additionally, the obvious seasonal residual perturbations in Southwest China may be related to the leakage errors in the GRACE data processing and large uncertainty in the land water storage in SLMs, indicating that GPS observations may provide more realistic mass transport estimates in Southwest China.

Have GRACE satellites overestimated groundwater depletion in the Northwest India Aquifer?

The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 ± 0.1 cm/a (or 14 ± 0.4 km3/a) for Jan 2005–Dec 2010, consistent with the GWD rate (2.8 cm/a or 12.3 km3/a) from groundwater-level monitoring data. Published studies (e.g., 4 ± 1 cm/a or 18 ± 4.4 km3/a) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.

Improved source parameter constraints for five undersea earthquakes from north component of GRACE gravity and gravity gradient change measurements

Abstract The innovative processing of Gravity Recovery And Climate Experiment (GRACE) data using only the north component of gravity change and its corresponding gravity gradient changes allows the enhancement of the spatial resolution for coseismic deformation signals. Here, we report the study of five undersea earthquakes using this technique: the 2004 Sumatra–Andaman earthquake, the 2007 Bengkulu earthquake, the 2010 Maule, Chile earthquake, the 2011 Tohoku earthquake, and the 2012 Indian Ocean earthquakes. By using the high spherical harmonic degree (up to degree 96) data products and the associated GRACE data processing techniques, the retrieved north component of gravity change is up to − 34 ± 1.4 μ Gal for the 2004 Sumatra–Andaman earthquake, which illustrates by far the highest amplitude of the coseismic signal retrieved from satellite gravimetry among previous studies. We creatively apply the localized spectral analysis as an efficient method to empirically determine the practical spherical harmonic truncation degree. By combining least squares adjustment with the simulated annealing algorithm, point source parameters are estimated, which demonstrates the unique constraint on source model from GRACE data compared to other data sources. For the 2004 Sumatra–Andaman earthquake, GRACE data produce a shallower centroid depth (9.1 km), as compared to the depth (28.3 km) from GPS data. For the 2011 Tohoku earthquake, the GRACE-estimated centroid location is southwest of the GPS/seismic solutions, and the slip orientation is about 10° clockwise from the published GPS/seismic slip models. We concluded that these differences demonstrate the additional and critical offshore constraint by GRACE on source parameters, as compared to GPS/seismic data.

Arctic Ocean tides measured by satellite

The Gravity Recovery and Climate Experiment (GRACE) measures Earth's gravity field by gauging the acceleration of two satellites that travel about 220 kilometers apart—slight differences in acceleration between the two satellites provide information about the distribution of mass below the satellites. To get an accurate picture of Earth's gravity field, it is necessary to know how tides are contributing to GRACE measurements. However, tidal corrections have usually been made using data from satellite altimeters that do not cover the Arctic. To get around this problem, Killett et al. developed a new method of analyzing GRACE data that enabled them to extract Arctic Ocean tides. They suggest that applying their method could help improve GRACE data processing. (Journal of Geophysical Research‐Oceans, doi:10.1029/2011JC007111, 2011)

Gravity Recovery and Climate Experiment (GRACE) 중력자료 해석을 위한 자료 처리 및 응용

2002년 4월에 발사된 Gravity Recovery and Climate Experiment(GRACE) 위성의 중력시간변화 측정을 통해, 기후 및 환경 변화에 의한 지구 내 질량 재배치 연구가 가능해 졌다. GRACE 중력 자료는 구면조화 함수의 계수인 중력 스펙트럼 형태로 제공이 되며, 이를 구면조화 함수를 이용하여 원하는 지역의 중력 변화 또는 질량 변화로 환산을 해야 한다. 하지만, GRACE 중력 자료는 측정 잡음 이외에도 공간적인 알리아스 에러가 존재하여, 질량 재배치 효과를 확인하기 위해서는 중력 스펙트럼의 처리 과정이 필요하다. 이 연구에서는 GRACE 자료를 처리하는 가장 일반적인 방법을 소개하고, 처리된 중력 자료를 이용한 연구 사례를 소개하였다. GRACE 중력 자료를 이용하여 광범위한 지구과학 연구가 진행 중이지만, 그 중 가장 활발한 연구 분야인 육지의 물수지 연구, 빙하 변화 연구 그리고 해수면 상승 연구 등을 중심으로 소개하였다. GRACE 위성과 유사한 인공위성 중력 관측 사업이 2020년까지 계획되어 있으며, 향후 수십년간 축적된 인공위성 중력 자료는 지구 환경 변화 연구에 핵심적인 자료로 활용될 것으로 기대된다. Gravity Recovery and Climate Experiment (GRACE), launched in April, 2002, makes it possible to monitor Earth's mass redistribution with its time-varying gravity observation. GRACE provides monthly gravity solutions as coefficients of spherical harmonics, and thus ones need to convert the gravity spectrum to gravity grids (or mass grids) via the spherical harmonics. GRACE gravity solutions, however, include spatial alias error as well as noise, which requires to suppress in order to enhance signal to noise ratio. In this study, we present the GRACE data processing procedures and introduce some applications of time-varying gravity, which are studies of terrestrial water storage changes, Antarctic and Greenland ice melting, and sea level rise. Satellite missions such as GRACE will continue up to early 2020, and they are expected to be an essential resource to understand the global climate changes.

GRACE hydrological monitoring of Australia: Current limitations and future prospects

The Gravity Recovery and Climate Experiment (GRACE) twin‐satellite gravimetry mission has been monitoring time‐varying changes of the Earth's gravitational field on a near‐global scale since 2002. One of the environmentally important signals to be detected is temporal variations induced by changes in the distribution of terrestrial water storage (i.e., hydrology). Since water is one of Australia's precious resources, it is logical to monitor its distribution, and GRACE offers one such opportunity. The second and fourth releases (referred to as RL02 and RL04) of the ‘standard’ monthly GRACE solutions with respect to their annual mean are analysed. When compared to rainfall data over the same time period, GRACE is shown to detect hydrological signals over Australia, with the RL04 data showing better results. However, the relatively small hydrological signal typical for much of Australia is obscured by deficiencies in the standard GRACE data processing and filtering methods. Spectral leakage of oceanic mass changes also still contaminates the small hydrological signals typical over land. It is therefore recommended that Australia‐focussed reprocessing of GRACE data is needed for useful hydrological signals to be extracted. Naturally, this will have to be verified by independent ‘in situ’ external sources such as rainfall, soil moisture and groundwater borehole piezometer data over Australia.

Comment on “On the steric and mass‐induced contributions to the annual sea level variations in the Mediterranean Sea” by David García et al.

[1] Garcia et al. [2006] (hereafter referred to as GCDVG) present an analysis of mass-induced sea level variation (SLVmass) of the Mediterranean Sea based on satellite altimetry, ocean modeling and Gravity Recovery and Climate Experiment (GRACE) gravity measurements. In particular, GCDVG find consistency between altimeter sea level variations corrected for steric effects and changes in oceanic mass derived from Gravity Recovery and Climate Experiment (GRACE). GCDVG show that the annual cycle of the sea level variability (SLVtotal) is dominated by changes in its steric part (SLVsteric) and that the basin average of mass (SLVmass) is maximum in February. They conclude that, when the sea level is rising (falling) the Mediterranean Sea is actually losing (gaining) mass. [2] GCDVG appear to use an unrealistically large estimate of steric sea level variability (SLVsteric) and neglect changes in continental water storage that can significantly affect GRACE measurements over the Mediterranean Sea. These issues are examined in turn below by reviewing relevant individual basin averages of the Mediterranean Sea in comparison to results of other studies. In particular, while also finding consistency between altimetry and GRACE measurements, Fenoglio-Marc et al. [2006] (hereafter referred to as FKB), in a similar study of the Mediterranean Sea, find steric effects only accounting for about 60% of the total annual sea level variability and the annual cycle of SLVmass having a maximum in November.

Signal and noise in Gravity Recovery and Climate Experiment (GRACE) observed surface mass variations

The Gravity Recovery and Climate Experiment (GRACE) product used for this study consists of 43 monthly potential coefficient sets released by the GRACE science team which are used to generate surface mass thickness grids expressed as equivalent water heights (EQWHs). We optimized both the smoothing radius and the level of approximation by empirical orthogonal functions (EOFs) and found that 6.25° and three modes are able to describe more than 73.5% of the variance. The EQWHs obtained by the EOF method describe all known variations in the continental hydrology, present‐day ice sheet melting, and global isostatic adjustment. To assess the quality of the estimated grids, we constructed degree error spectra of EQWHs. We conclude that a significant part of the errors in GRACE can be explained by a scaling factor of 0.85 relative to degree error estimates provided by the GGM02C gravity model but that the present‐day errors in the GRACE data are a factor 2 to 5 larger than forecasted by tide model differences and atmospheric pressure differences. Comparison to a network of 59 International GNSS Service (IGS) stations confined the filter parameter settings to three EOF modes and 5° or 6.25° smoothing radius. Residuals that remain after the EOF method do exhibit S2 aliasing errors and a semiannual continental hydrology signal contained in the Global Land Data Assimilation Systems (GLDAS) model. Further analysis of the residual EOF signal revealed alternating track correlation patterns that are partially explained by the GRACE covariance matrix and the handling of nuisance parameters in the GRACE data processing.

Seasonal variation of ocean bottom pressure derived from Gravity Recovery and Climate Experiment (GRACE): Local validation and global patterns

The Gravity Recovery and Climate Experiment (GRACE) processing centers at the GeoForschungsZentrum Potsdam (GFZ) and the University of Texas Center for Space Research (UTCSR) provide time series of monthly gravity field solutions covering the period since mission launch in March 2002. Although the achieved accuracy still remains an order of magnitude below the mission's baseline goal, these time series have successfully been used to study terrestrial phenomena such as water storage variations. Over the oceans, the monthly gravity field solutions can be converted into estimates of the fluctuating ocean bottom pressure (OBP), which is the sum of atmospheric and oceanic mass variations. The GRACE products may be validated against in situ OBP observations which are available from a ground truth site in the tropical northwest Atlantic Ocean. Large differences are observed between the in situ and GRACE‐derived OBP which are investigated by comparing the tidal and nontidal ocean models used at GFZ and UTCSR for dealiasing short‐term (<2 months) mass variations from satellite measurements. Results show that the barotropic nontidal and tide models need improvement at periods shorter than 1 day and longer than 2 weeks. On a global scale the monthly OBP fields from GRACE generally overestimate the variability compared to ocean general circulation models, especially in tropical regions. This may be attributed to continuing deficiencies in GRACE data processing. Nevertheless, there is some initial evidence that GRACE possesses the potential to observe large‐scale averages of bottom pressure fluctuations.

Low degree gravitational changes from GRACE: Validation and interpretation

We examine low degree gravitational variations ΔC21, ΔS21, and ΔC20 observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during the first 2 years of this gravity mission. The GRACE observations are compared with independent estimates from accurately measured Earth rotational changes and predictions from atmospheric, oceanic, and hydrological models. The 18 GRACE monthly gravity solutions, covering the period April 2002 to March 2004, show strong seasonal variability in the ΔC21, ΔS21, and ΔC20 time series, and generally agree with Earth rotation‐derived changes and geophysical model estimates, in particular for ΔS21 and ΔC20. The reason for the poorer agreement between the GRACE results and the Earth rotation‐derived estimates for ΔC21 is unclear. We demonstrate that the omission of the ocean pole tide in the GRACE data processing does have significant effects on the estimated ΔC21 and ΔS21.