Gravity Recovery And Climate Experiment Estimates Research Articles

Satellite Gravity Measurements Confirm Accelerated Melting of Greenland Ice Sheet

Using time-variable gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, we estimate ice mass changes over Greenland during the period April 2002 to November 2005. After correcting for the effects of spatial filtering and limited resolution of GRACE data, the estimated total ice melting rate over Greenland is -239 +/- 23 cubic kilometers per year, mostly from East Greenland. This estimate agrees remarkably well with a recent assessment of -224 +/- 41 cubic kilometers per year, based on satellite radar interferometry data. GRACE estimates in southeast Greenland suggest accelerated melting since the summer of 2004, consistent with the latest remote sensing measurements.

Assessing a land surface model's improvements with GRACE estimates

The Gravity Recovery and Climate Experiment (GRACE) satellites have produced an unprecedented data set of terrestrial water storage (TWS) change in large‐scale river basins. Recent research has found that monthly variations of soil moisture and snow water simulated by land surface models compared favorably with the GRACE‐derived TWS change. Compared to the GRACE data, the standard version of the National Center for Atmospheric Research (NCAR) Community Land Model (CLM) produces a weaker TWS variability in tropical and mid‐latitudes but a stronger TWS variation in high latitudes. However, a modified version of CLM that includes more realistic interception, runoff, and frozen soil processes improves the simulation of TWS change in global river basins of various scales. In addition, the modified CLM improves the modeling of evapotranspiration through the improvements in the modeling of TWS variation and runoff in the Amazon River basin. Along this line of research, this paper shows that such GRACE data can be used as a means of evaluating the hydrological schemes in a land surface model.

Estimating Large-Scale Precipitation Minus Evapotranspiration from GRACE Satellite Gravity Measurements

Abstract Currently, observations of key components of the earth's large-scale water and energy budgets are sparse or even nonexistent. One key component, precipitation minus evapotranspiration (P − ET), remains largely unmeasured due to the absence of observations of ET. Precipitation minus evapotranspiration describes the flux of water between the atmosphere and the earth's surface, and therefore provides important information regarding the interaction of the atmosphere with the land surface. In this paper, large-scale changes in continental water storage derived from satellite gravity data from the Gravity Recovery and Climate Experiment (GRACE) project are combined with river discharge data to obtain estimates of areally averaged P − ET. After constructing an equation describing the large-scale terrestrial water balance reflecting the temporal sampling of GRACE water storage estimates, GRACE-derived P − ET estimates are compared to those obtained from a reanalysis dataset [NCEP/Department of Energy (DOE) reanalysis-2] and a land surface model driven with observation-based forcing [Global Land Data Assimilation System (GLDAS)/Noah] for two large U.S. river basins. GRACE-derived P − ET compares quite favorably with the reanalysis-2 output, while P − ET from the Noah model shows significant differences. Because the uncertainties in the GRACE results can be computed rigorously, this comparison may be considered as a validation of the models. In addition to showing how GRACE P − ET estimates may be used to validate model output, the accuracy of GRACE estimates of both the seasonal cycle and the monthly averaged rate of P − ET is examined. Finally, the potential for estimating seasonal evapotranspiration is demonstrated by combining GRACE seasonal P − ET estimates with independent estimates of the seasonal cycle of precipitation.

Non-isotropic filtering of GRACE temporal gravity for geophysical signal enhancement

SUMMARY Monthly mass variations within the Earth system produce temporal gravity changes, which are observable by the NASA/GFZ Gravity Recovery and Climate Experiment (GRACE) twin-satellite system. Mass load changes with spatial scales larger than 1000 km have been observed using conventional filters based on a Gaussian smoother, which applies a weight to GRACE spherical harmonic (SH) coefficients depending only on SH degree. This practice is consistent with a degree-dependent error model for GRACE monthly geopotential solutions. The Gaussian filters effectively dampen all power of ill-determined higher-degree components in the estimates. However, the spatial sampling provided by GRACE yields errors that vary with both SH degree and order. The consequence is that maps of spatial loads shall not be smoothed with an isotropic (degree-only) filter, but shall be constructed using anisotropic smoothing thus also yielding better spatial resolution in latitude. We have developed a non-isotropic filter to optimize the smoothing of GRACE temporal gravity observations by considering the degree- and order-dependent quality of GRACE estimates, the latter analysed from the correlation with the predicted signals of hydrologic and ocean models. In order to retain GRACE coefficients in the filtering process that show reasonable correlation with the geophysical (hydrology and ocean) models, we applied Gaussian-type smoothing but with averaging radius depending on the order of the geopotential coefficient estimates. Applied to 2 yr of GRACE data, we showed that the resulting non-isotropic filter yields enhanced GRACE signals with significantly higher resolution in latitude and the same resolution in longitude without reducing the accuracy as compared to the conventional Gaussian smoother.

Basin scale estimates of evapotranspiration using GRACE and other observations

Evapotranspiration is integral to studies of the Earth system, yet it is difficult to measure on regional scales. One estimation technique is a terrestrial water budget, i.e., total precipitation minus the sum of evapotranspiration and net runoff equals the change in water storage. Gravity Recovery and Climate Experiment (GRACE) satellite gravity observations are now enabling closure of this equation by providing the terrestrial water storage change. Equations are presented here for estimating evapotranspiration using observation based information, taking into account the unique nature of GRACE observations. GRACE water storage changes are first substantiated by comparing with results from a land surface model and a combined atmospheric‐terrestrial water budget approach. Evapotranspiration is then estimated for 14 time periods over the Mississippi River basin and compared with output from three modeling systems. The GRACE estimates generally lay in the middle of the models and may provide skill in evaluating modeled evapotranspiration.

Estimated accuracies of regional water storage variations inferred from the Gravity Recovery and Climate Experiment (GRACE)

The satellite Gravity Recovery and Climate Experiment (GRACE) provides data describing monthly changes in the geoid, which are closely related to changes in vertically integrated terrestrial water storage. Unlike conventional point or gridded hydrologic measurements, such as those from rain gauges, stream gauges, rain radars, and radiometric satellite images, GRACE data are sets of Stokes coefficients in a truncated spherical harmonic expansion of the geoid. Swenson and Wahr [2002] describe techniques for constructing spatial averaging kernels, with which the average change in vertically integrated water storage within a given region can be extracted from a set of Stokes coefficients. This study extends that work by applying averaging kernels to a realistic synthetic GRACE gravity signal derived in part from a large‐scale hydrologic model. By comparing the water storage estimates inferred from the synthetic GRACE data with the water storage estimates predicted by the same hydrologic model, we are able to assess the accuracy of the GRACE estimates and to compare the performance of different averaging kernels. We focus specifically on recovering monthly water storage variations within North American river basins. We conclude that GRACE will be capable of estimating monthly changes in water storage to accuracies of better than 1 cm of water thickness for regions having areas of 4.0 · 105 km2 or larger. Accuracies are better for larger regions. The water storage signal of the Mississippi river basin (area = 3.9 · 106 km2), for example, can be obtained to better than 5 mm. For regional‐ to global‐scale water balance analyses, this result indicates that GRACE will provide a useful, direct measure of seasonal water storage for river‐basin water balance analyses; such data are without precedent in hydrologic analysis.