Reconstructing GRACE-type time-variable gravity from the Swarm satellites

AbstractThe Gravity Recovery and Climate Experiment (GRACE) mission has enabled mass changes and transports in the hydrosphere, cryosphere and oceans to be quantified with unprecedented resolution. However, while this legacy is currently being continued with the GRACE Follow-On (GRACE-FO) mission there is a gap of 11 months between the end of GRACE and the start of GRACE-FO which must be addressed. Here we bridge the gap by combining time-variable, low-resolution gravity models derived from European Space Agency’s Swarm satellites with the dominating spatial modes of mass variability obtained from GRACE. We show that the noise inherent in unconstrained Swarm gravity solutions is greatly reduced, that basin averages can have root mean square errors reduced to the order of $$\text {cm}$$ cm of equivalent water height, and that useful information can be retrieved for basins as small as $$1000 \times 1000\,\hbox {km}$$ 1000 × 1000 km . It is found that Swarm data contains sufficient information to inform the leading three global mass modes found in GRACE at the least. By comparing monthly reconstructed maps to GRACE data from December 2013 to June 2017, we suggest the uncertainty of these maps to be $$2{-}3\,\text {cm}$$ 2 - 3 cm of equivalent water height.

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Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

10.5194/se-2017-127 ◽

2017 ◽

Author(s):

Christina Lück ◽

Jürgen Kusche ◽

Roelof Rietbroek ◽

Anno Löcher

Keyword(s):

Time Series ◽

Gravity Field ◽

Sea Level ◽

Time Variable ◽

Field Analysis ◽

Time Variable Gravity ◽

Ocean Mass ◽

Grace Data ◽

Swarm Satellites ◽

Variable Gravity

Abstract. Measuring the spatiotemporal variation of ocean mass allows one to partition volumetric sea level change, sampled by radar altimeters, into a mass-driven and a steric part, the latter being related to ocean heat change and the current Earth’s energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission provides estimates of the Earth’s time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites; i.e. extending the GRACE time series. Here we utilize data from the European Space Agency’s (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. We investigate the potential to bridge the gap between the GRACE missions and to substitute missing monthly solutions. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating trend, annual and semiannual signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, for 80.0 % of all investigated cases of an 18-months-gap, Swarm ocean mass was found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modelling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including non-dedicated satellites.

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Time-variable gravity fields and ocean mass change from 37 months of kinematic Swarm orbits

Solid Earth ◽

10.5194/se-9-323-2018 ◽

2018 ◽

Vol 9 (2) ◽

pp. 323-339 ◽

Cited By ~ 15

Author(s):

Christina Lück ◽

Jürgen Kusche ◽

Roelof Rietbroek ◽

Anno Löcher

Keyword(s):

Time Series ◽

Gravity Field ◽

Sea Level ◽

Time Variable ◽

Time Variable Gravity ◽

Ocean Mass ◽

Grace Data ◽

Swarm Satellites ◽

Gravity Fields ◽

Variable Gravity

Abstract. Measuring the spatiotemporal variation of ocean mass allows for partitioning of volumetric sea level change, sampled by radar altimeters, into mass-driven and steric parts. The latter is related to ocean heat change and the current Earth's energy imbalance. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has provided monthly snapshots of the Earth's time-variable gravity field, from which one can derive ocean mass variability. However, GRACE has reached the end of its lifetime with data degradation and several gaps occurred during the last years, and there will be a prolonged gap until the launch of the follow-on mission GRACE-FO. Therefore, efforts focus on generating a long and consistent ocean mass time series by analyzing kinematic orbits from other low-flying satellites, i.e. extending the GRACE time series. Here we utilize data from the European Space Agency's (ESA) Swarm Earth Explorer satellites to derive and investigate ocean mass variations. For this aim, we use the integral equation approach with short arcs (Mayer-Gürr, 2006) to compute more than 500 time-variable gravity fields with different parameterizations from kinematic orbits. We investigate the potential to bridge the gap between the GRACE and the GRACE-FO mission and to substitute missing monthly solutions with Swarm results of significantly lower resolution. Our monthly Swarm solutions have a root mean square error (RMSE) of 4.0 mm with respect to GRACE, whereas directly estimating constant, trend, annual, and semiannual (CTAS) signal terms leads to an RMSE of only 1.7 mm. Concerning monthly gaps, our CTAS Swarm solution appears better than interpolating existing GRACE data in 13.5 % of all cases, when artificially removing one solution. In the case of an 18-month artificial gap, 80.0 % of all CTAS Swarm solutions were found closer to the observed GRACE data compared to interpolated GRACE data. Furthermore, we show that precise modeling of non-gravitational forces acting on the Swarm satellites is the key for reaching these accuracies. Our results have implications for sea level budget studies, but they may also guide further research in gravity field analysis schemes, including satellites not dedicated to gravity field studies.

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Spatio-Temporal Patterns of Mass Changes in Himalayan Glaciated Region from EOF Analyses of GRACE Data

Remote Sensing ◽

10.3390/rs13020265 ◽

2021 ◽

Vol 13 (2) ◽

pp. 265

Author(s):

Harika Munagapati ◽

Virendra M. Tiwari

Keyword(s):

Temperature Anomaly ◽

Mass Gain ◽

Snow Accumulation ◽

Empirical Orthogonal Functions ◽

Total Water ◽

Orthogonal Functions ◽

Grace Data ◽

Grace Satellite ◽

Spatio Temporal ◽

Gravity Recovery

The nature of hydrological seasonality over the Himalayan Glaciated Region (HGR) is complex due to varied precipitation patterns. The present study attempts to exemplify the spatio-temporal variation of hydrological mass over the HGR using time-variable gravity from the Gravity Recovery and Climate Experiment (GRACE) satellite for the period of 2002–2016 on seasonal and interannual timescales. The mass signal derived from GRACE data is decomposed using empirical orthogonal functions (EOFs), allowing us to identify the three broad divisions of HGR, i.e., western, central, and eastern, based on the seasonal mass gain or loss that corresponds to prevailing climatic changes. Further, causative relationships between climatic variables and the EOF decomposed signals are explored using the Granger causality algorithm. It appears that a causal relationship exists between total precipitation and total water storage from GRACE. EOF modes also indicate certain regional anomalies such as the Karakoram mass gain, which represents ongoing snow accumulation. Our causality result suggests that the excessive snowfall in 2005–2008 has initiated this mass gain. However, as our results indicate, despite the dampening of snowfall rates after 2008, mass has been steadily increasing in the Karakorum, which is attributed to the flattening of the temperature anomaly curve and subsequent lower melting after 2008.

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Evaluation of GRACE/GRACE Follow-On Time-Variable Gravity Field Models for Earthquake Detection above Mw8.0s in Spectral Domain

Remote Sensing ◽

10.3390/rs13163075 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3075

Author(s):

Ming Xu ◽

Xiaoyun Wan ◽

Runjing Chen ◽

Yunlong Wu ◽

Wenbing Wang

Keyword(s):

Signal To Noise Ratio ◽

Time Variable ◽

Model Errors ◽

Signal To Noise ◽

Time Variable Gravity ◽

Earthquake Detection ◽

Gravity Fields ◽

Variable Gravity ◽

Gravity Recovery ◽

Gravity Field Models

This study compares the Gravity Recovery And Climate Experiment (GRACE)/GRACE Follow-On (GFO) errors with the coseismic gravity variations generated by earthquakes above Mw8.0s that occurred during April 2002~June 2017 and evaluates the influence of monthly model errors on the coseismic signal detection. The results show that the precision of GFO monthly models is approximately 38% higher than that of the GRACE monthly model and all the detected earthquakes have signal-to-noise ratio (SNR) larger than 1.8. The study concludes that the precision of the time-variable gravity fields should be improved by at least one order in order to detect all the coseismic gravity signals of earthquakes with M ≥ 8.0. By comparing the spectral intensity distribution of the GFO stack errors and the 2019 Mw8.0 Peru earthquake, it is found that the precision of the current GFO monthly model meets the requirement to detect the coseismic signal of the earthquake. However, due to the limited time length of the observations and the interference of the hydrological signal, the coseismic signals are, in practice, difficult to extract currently.

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Preliminary monthly gravity field models from kinematic orbits of SWARM Satellites

10.5194/egusphere-egu21-9839 ◽

2021 ◽

Author(s):

Xingfu Zhang ◽

Qiujie Chen ◽

Yunzhong Shen

Keyword(s):

Gravity Field ◽

Large Scale ◽

Earth System ◽

The Earth ◽

Swarm Satellites ◽

Variable Gravity ◽

Data Gap ◽

One Year ◽

Gravity Recovery ◽

Gravity Field Models

&#160; &#160; &#160; Although the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE FO) satellite missions play an important role in monitoring global mass changes within the Earth system, there is a data gap of about one year spanning July 2017 to May 2018, which leads to discontinuous gravity observations for monitoring global mass changes. As an alternative mission, the SWARM satellites can provide gravity observations to close this data gap. In this paper, we are dedicated to developing alternative monthly time-variable gravity field solutions from SWARM data. Using kinematic orbits of SWARM from ITSG for the period January 2015 to September 2020, we have generated a preliminary time series of monthly gravity field models named Tongji-Swarm2019 up to degree and order 60. The comparisons between Tongji-Swarm2019 and GRACE/GRACE-FO monthly solutions show that Tongji-Swarm2019 solutions agree with GRACE/GRACE-FO models in terms of large-scale mass change signals over amazon, Greenland and other regions. We can conclude that Tongji-Swarm2019 monthly gravity field models are able to close the gap between GRACE and GRACE FO.

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A framework for deriving drought indicators from the Gravity Recovery and Climate Experiment (GRACE)

Hydrology and Earth System Sciences ◽

10.5194/hess-24-227-2020 ◽

2020 ◽

Vol 24 (1) ◽

pp. 227-248 ◽

Cited By ~ 2

Author(s):

Helena Gerdener ◽

Olga Engels ◽

Jürgen Kusche

Keyword(s):

Hydrological Cycle ◽

Water Storage ◽

Synthetic Data ◽

Satellite Gravity ◽

Spatially Correlated ◽

Grace Data ◽

Spatial Noise ◽

Drought Indicators ◽

Gravity Recovery ◽

Linear Trends

Abstract. Identifying and quantifying drought in retrospective is a necessity for better understanding drought conditions and the propagation of drought through the hydrological cycle and eventually for developing forecast systems. Hydrological droughts refer to water deficits in surface and subsurface storage, and since these are difficult to monitor at larger scales, several studies have suggested exploiting total water storage data from the GRACE (Gravity Recovery and Climate Experiment) satellite gravity mission to analyze them. This has led to the development of GRACE-based drought indicators. However, it is unclear how the ubiquitous presence of climate-related or anthropogenic water storage trends found within GRACE analyses masks drought signals. Thus, this study aims to better understand how drought signals propagate through GRACE drought indicators in the presence of linear trends, constant accelerations, and GRACE-specific spatial noise. Synthetic data are constructed and existing indicators are modified to possibly improve drought detection. Our results indicate that while the choice of the indicator should be application-dependent, large differences in robustness can be observed. We found a modified, temporally accumulated version of the Zhao et al. (2017) indicator particularly robust under realistic simulations. We show that linear trends and constant accelerations seen in GRACE data tend to mask drought signals in indicators and that different spatial averaging methods required to suppress the spatially correlated GRACE noise affect the outcome. Finally, we identify and analyze two droughts in South Africa using real GRACE data and the modified indicators.

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Multiple-data-based monthly geopotential model set LDCmgm90

Scientific Data ◽

10.1038/s41597-019-0239-7 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 3

Author(s):

Wei Chen ◽

Jiesi Luo ◽

Jim Ray ◽

Nan Yu ◽

Jian Cheng Li

Keyword(s):

Gravity Data ◽

Gravity Models ◽

Geopotential Model ◽

Satellite Mission ◽

Stripe Pattern ◽

Angular Momenta ◽

Multiple Data ◽

Model Set ◽

Gravity Recovery

Abstract While the GRACE (Gravity Recovery and Climate Experiment) satellite mission is of great significance in understanding various branches of Earth sciences, the quality of GRACE monthly products can be unsatisfactory due to strong longitudinal stripe-pattern errors and other flaws. Based on corrected GRACE Mascon (mass concentration) gridded mass transport time series and updated LDCgam (Least Difference Combination global angular momenta) data, we present a new set of monthly gravity models called LDCmgm90, in the form of Stokes coefficients with order and degree both up to 90. The LDCgam inputs are developed by assimilating degree-2 Stokes coefficients from various versions of GRACE (including Mascon products) and SLR (Satellite Laser Ranging) monthly gravity data into combinations of outputs from various global atmospheric, oceanic, and hydrological circulation models, under the constraints of accurately measured Earth orientation parameters in the Least Difference Combination (LDC) scheme. Taking advantages of the relative strengths of the various input solutions, the LDCmgm90 is free of stripes and some other flaws of classical GRACE products.

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The uncertainty of storage-based drought indices from GRACE

10.5194/egusphere-egu2020-3604 ◽

2020 ◽

Author(s):

Peyman Saemian ◽

Mohammad Javad Tourian ◽

Nico Sneeuw

Keyword(s):

Land Surface ◽

Water Storage ◽

Extended Period ◽

Drought Indices ◽

Terrestrial Water Storage ◽

Earth’S Gravity Field ◽

Grace Data ◽

Temporal And Spatial Variability ◽

Terrestrial Water ◽

Gravity Recovery

Climate change and the growing demand for freshwater have raised the frequency and intensity of extreme events like drought. Satellite observations have improved our understanding of the temporal and spatial variability of droughts. Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) have been observing variations in Earth's gravity field yielding valuable information about changes in terrestrial water storage anomaly (TWSA). The terrestrial water storage vertically integrates all forms of water on and beneath land surface including snow, surface water, soil moisture, and groundwater storage.Drought indices help to monitor drought by characterizing it in terms of their severity, location, duration and timing. Several drought indices have been developed based on GRACE water storage anomaly from a GRACE-based climatology, most of which suffer from the short record of GRACE, about 15 years, for their climatology. The limited duration of the GRACE observations necessitates the use of external datasets of TWSA with a more extended period for climatology. Drought characterization comes with its own uncertainties due to the inherent uncertainty in the GRACE data, the various post-processing approaches of GRACE data, and different options for external datasets on the other hand.This study offers a method to quantify uncertainties for the storage-based drought index. Moreover, we assess the sensitivity of major global river basins to the duration of the observations. The outcome of the study is invaluable in the sense that it allows for a more informative storage based drought, including uncertainty, thus enabling a more realistic risk assessment.

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An ice flow modeling perspective on bedrock adjustment patterns of the Greenland ice sheet

The Cryosphere ◽

10.5194/tc-6-1263-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 1263-1274 ◽

Cited By ~ 3

Author(s):

M. Olaizola ◽

R. S. W. van de Wal ◽

M. M. Helsen ◽

B. de Boer

Keyword(s):

Mass Balance ◽

Maximum Yield ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Elastic Response ◽

Delayed Response ◽

Surface Mass ◽

Grace Data ◽

Grace Satellites ◽

Gravity Recovery

Abstract. Since the launch in 2002 of the Gravity Recovery and Climate Experiment (GRACE) satellites, several estimates of the mass balance of the Greenland ice sheet (GrIS) have been produced. To obtain ice mass changes, the GRACE data need to be corrected for the effect of deformation changes of the Earth's crust. Recently, a new method has been proposed where ice mass changes and bedrock changes are simultaneously solved. Results show bedrock subsidence over almost the entirety of Greenland in combination with ice mass loss which is only half of the currently standing estimates. This subsidence can be an elastic response, but it may however also be a delayed response to past changes. In this study we test whether these subsidence patterns are consistent with ice dynamical modeling results. We use a 3-D ice sheet–bedrock model with a surface mass balance forcing based on a mass balance gradient approach to study the pattern and magnitude of bedrock changes in Greenland. Different mass balance forcings are used. Simulations since the Last Glacial Maximum yield a bedrock delay with respect to the mass balance forcing of nearly 3000 yr and an average uplift at present of 0.3 mm yr−1. The spatial pattern of bedrock changes shows a small central subsidence as well as more intense uplift in the south. These results are not compatible with the gravity based reconstructions showing a subsidence with a maximum in central Greenland, thereby questioning whether the claim of halving of the ice mass change is justified.

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Comparing Groundwater Storage Changes in Two Main Grain Producing Areas in China: Implications for Sustainable Agricultural Water Resources Management

Remote Sensing ◽

10.3390/rs12132151 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2151 ◽

Cited By ~ 3

Author(s):

Longqun Zheng ◽

Yun Pan ◽

Huili Gong ◽

Zhiyong Huang ◽

Chong Zhang

Keyword(s):

Food Production ◽

Groundwater Abstraction ◽

Groundwater Storage ◽

The North ◽

Grace Data ◽

Groundwater Supply ◽

Agricultural Water Resources ◽

Gravity Recovery ◽

Sown Area ◽

Additional Area

Balancing groundwater supply and food production is challenging, especially in large regions where there is often insufficient information on the groundwater budget, such as in the North China Plain (NCP) and the Northeast China Plain (NECP), which are major food producing areas in China. This study aimed to understand this process in a simple but efficient way by using Gravity Recovery and Climate Experiment (GRACE) data, and it focused on historical and projected groundwater storage (GWS) changes in response to changes in grain-sown areas. The results showed that during 2003–2016, the GWS was depleted in the NCP at a rate of −17.2 ± 0.8 mm/yr despite a decrease in groundwater abstraction along with an increase in food production and a stable sown area, while in the NECP, the GWS increased by 2.3 ± 0.7 mm/yr and the groundwater abstraction, food production and the sown area also increased. The scenario simulation using GRACE-derived GWS anomalies during 2003–2016 as the baseline showed that the GWS changes in the NCP can be balanced (i.e., no decreasing trend in storage) by reducing the area of winter wheat and maize by 1.31 × 106 ha and 3.21 × 106 ha, respectively, or by reducing both by 0.93 × 106 ha. In the NECP, the groundwater can sustain an additional area of 0.62 × 106 ha of maize without a decrease in storage. The results also revealed that the current groundwater management policies cannot facilitate the recovery of the GWS in the NCP unless the sown ratio of drought-resistance wheat is increased from 90% to 95%. This study highlights the effectiveness of using GRACE to understanding the nexus between groundwater supply and food production at large scales.

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