Estimating geocenter variations from a combination of GRACE and ocean model output

Swenson, Sean; Chambers, D. P.; Wahr, John

doi:10.1029/2007jb005338

Cited by 778 publications

(690 citation statements)

References 33 publications

Supporting

Mentioning

681

Contrasting

Unclassified

Order By: Relevance

“…TWS obs is derived from the GRACE Tellus Mascon product version 2 based on the GRACE gravity fields Release 05, processed at NASA's Jet Propulsion Laboratory (JPL) (Watkins et al, 2015;Wiese, 2015). The GRACE solutions were corrected for geocentric motion coefficients, according to Swenson et al (2008), and for variations in Earth's oblateness (C20 coefficient) obtained from satellite laser ranging (Cheng et al, 2013). The glacial isostatic adjustment has been accounted for using the model by A et al (2013).…”

Section: Input Datamentioning

confidence: 99%

Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid-to high latitudes.To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales.Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid-to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model-data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on interannual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short-and long-term variability of water resources.

show abstract

Section: Input Datamentioning

confidence: 99%

Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…The monthly degree 2, order 0 coefficients estimated with GRACE are replaced with those from a satellite laser ranging analysis (Cheng and Tapley, 2004), due to significant errors in observing that coefficient with GRACE. Monthly geocenter estimates based on the method of Swenson et al (2008) have been applied, as GRACE does not detect these. The method is based on a combination of GRACE gravity coefficients over the land and ice sheets and OBP from a model, including mean ocean mass variability.…”

Section: Review Of Release-04 Data Processing and Ocean Modelsmentioning

confidence: 99%

“…2, with the exception that the geocenter estimates are based on RL05 GRACE gravity data combined with RL05 Atmosphere-Ocean Dealiasing (AOD) OBP from the GAD files using the method described in Swenson et al (2008). The C 2,0 coefficients in the GFZ RL05 solutions are considerably closer to the SLR estimates than either the CSR RL05 or JPL RL05 solutions, likely because GFZ uses a background time-variable gravity model based on RL04 coefficients where the C 2,0 value had been replaced with that from SLR.…”

Section: Analysis Of Release-05 Datamentioning

confidence: 99%

Evaluation of Release-05 GRACE time-variable gravity coefficients over the ocean

2012

View full text Add to dashboard Cite

Abstract. The latest release of GRACE (Gravity Recovery and Climate Experiment) gravity field coefficients (Release-05, or RL05) are evaluated for ocean applications. Data have been processed using the current methodology for Release-04 (RL04) coefficients, and have been compared to output from two different ocean models. Results indicate that RL05 data from the three Science Data Centers -the Center for Space Research (CSR), GeoForschungsZentrum (GFZ), and Jet Propulsion Laboratory (JPL) -are more consistent among themselves than the previous RL04 data. Moreover, the variance of residuals with the output of an ocean model is 50-60 % lower for RL05 data than for RL04 data. A more optimized destriping algorithm is also tested, which improves the results slightly. By comparing the GRACE maps with two different ocean models, we can better estimate the uncertainty in the RL05 maps. We find the standard error to be about 1 cm (equivalent water thickness) in the low-and midlatitudes, and between 1.5 and 2 cm in the polar and subpolar oceans, which is comparable to estimated uncertainty for the output from the ocean models.

show abstract

“…We use the mascon solutions in the version which applies the Coastline Resolution Improvement (CRI) filter, for more information see [39]. The GRACE solutions were corrected for geocentric motion coefficients, according to [42] and for variations in Earth's oblateness (C20 coefficient) obtained from Satellite Laser Ranging [43]. The Glacial isostatic adjustment has been accounted for using the model by [44].…”

Section: Grace-derived ∆Twsmentioning

confidence: 99%

“…The GRACE solutions were corrected for geocentric motion coefficients, according to [42] and for variations in Earth's oblateness (C20 coefficient) obtained from Satellite Laser Ranging [43]. The Glacial isostatic adjustment has been accounted for using the model by [44].…”

Section: Grace-derived ∆Twsmentioning

confidence: 99%

Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead

et al. 2016

View full text Add to dashboard Cite

Abstract:The hydrological budget of a region is determined based on the horizontal and vertical water fluxes acting in both inward and outward directions. These integrated water fluxes vary, altering the total water storage and consequently the gravitational force of the region. The time-dependent gravitational field can be observed through the Gravity Recovery and Climate Experiment (GRACE) gravimetric satellite mission, provided that the mass variation is above the sensitivity of GRACE. This study evaluates mass changes in prominent reservoir regions through three independent approaches viz. fluxes, storages, and gravity, by combining remote sensing products, in-situ data and hydrological model outputs using WaterGAP Global Hydrological Model (WGHM) and Global Land Data Assimilation System (GLDAS). The results show that the dynamics revealed by the GRACE signal can be better explored by a hybrid method, which combines remote sensing-based reservoir volume estimates with hydrological model outputs, than by exclusive model-based storage estimates. For the given arid/semi-arid regions, GLDAS based storage estimations perform better than WGHM.

show abstract

Estimating geocenter variations from a combination of GRACE and ocean model output

Cited by 778 publications

References 33 publications

Understanding terrestrial water storage variations in northern latitudes across scales

Understanding terrestrial water storage variations in northern latitudes across scales

Evaluation of Release-05 GRACE time-variable gravity coefficients over the ocean

Water Budget Analysis within the Surrounding of Prominent Lakes and Reservoirs from Multi-Sensor Earth Observation Data and Hydrological Models: Case Studies of the Aral Sea and Lake Mead

Contact Info

Product

Resources

About