Seasonal and interannual global surface mass variations from multisatellite geodetic data

Wu, Xiaoping; Heflin, M. B.; Ivins, E. R.; Fukumori, Ichiro

doi:10.1029/2005jb004100

Cited by 82 publications

(103 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Half or a bit more of the observed annual signal is driven by seasonal variations in environmental surface loads (see, for example, Dong et al 2002;Davis et al 2004;Blewitt et al 2001;Kusche and Schrama 2005;Horwath et al 2010;Wu et al 2006;van Dam et al 2007, etc.). Thermal deformation of the GPS monuments and the bedrock to which they are attached probably also contribute an annual signal (Dong et al 2002;Yan et al 2009).…”

Section: Seasonal Signalsmentioning

confidence: 99%

Nontidal ocean loading: amplitudes and potential effects in GPS height time series

Dam

Collilieux

Wuite

et al. 2012

J Geod

108

View full text Add to dashboard Cite

Ocean bottom pressure (OBP) changes are caused by a redistribution of the ocean's internal mass that are driven by atmospheric circulation, a change in the mass entering or leaving the ocean, and/or a change in the integrated atmospheric mass over the ocean areas. The only previous global analysis investigating the magnitude of OBP surface displacements used older OBP data sets (van Dam et al. in J Geophys Res 129:507-517, 1997). Since then significant improvements in meteorological forcing models used to predict OBP have been made, augmented by observations from satellite altimetry and expendable bathythermograph profiles. Using more recent OBP estimates from the Estimating the Circulation and Climate of the Ocean (ECCO) project, we reassess the amplitude of the predicted effect of OBP on the height coordinate time series from a global distribution of GPS stations. OBP-predicted loading effects display an RMS scatter in the height of between 0.2 and 3.7 mm, larger than previously reported but still much smaller (by a factor of 2) than the scatter observed due to atmospheric pressure loading. Given the improvement in GPS hardware and data analysis techniques, the OBP signal is similar to the precision of weekly GPS height coordinates. We estimate the effect of OBP on GPS height coordinate time series using the MIT reprocessed solution, mi1. When we compare the predicted OBP height time series with mi1, we find that the scatter is reduced over all stations by 0.1 mm on average with reductions as high as 0.7 mm at some stations. More importantly we are able to reduce the scatter on 65 % of the stations investigated. The annual component of the OBP signal is responsible for 80 % of the reduction in scatter on average. We find that stations located close to semi-enclosed bays or seas are affected by OBP loading to a greater extent than other stations.

show abstract

Section: Seasonal Signalsmentioning

confidence: 99%

Nontidal ocean loading: amplitudes and potential effects in GPS height time series

Dam

Collilieux

Wuite

et al. 2012

J Geod

108

View full text Add to dashboard Cite

show abstract

“…Cheng et al 2013b;Ries 2013;Sośnica et al 2013) and GPSbased inversion (∼3 to ∼4 mm) (e.g. Wu et al 2006;Jansen et al 2009;Rietbroek et al 2012). Sun et al (2016a,b) developed an improved variant of the GRACE-OBP-Swenson approach (GRACE-OBP-Improved approach) by making a proper truncation of the input GRACE solutions, reducing GRACE signal leakage and taking into account self-attraction and loading (SAL) effects (Gordeev et al 1977;Conrad & Hager 1997).…”

Section: Introductionmentioning

confidence: 99%

Statistically optimal estimation of degree-1 and C20 coefficients based on GRACE data and an ocean bottom pressure model

Sun¹,

Ditmar²,

Riva³

2017

Geophysical Journal International

View full text Add to dashboard Cite

S U M M A R YIn this study, we develop a methodology to estimate monthly variations in degree-1 and C 20 coefficients by combing Gravity Recovery and Climate Experiment (GRACE) data with oceanic mass anomalies (combination approach). With respect to the method by Swenson et al., the proposed approach exploits noise covariance information of both input data sets and thus produces stochastically optimal solutions supplied with realistic error information. Numerical simulations show that the quality of degree-1 and -2 coefficients may be increased in this way by about 30 per cent in terms of RMS error. We also proved that the proposed approach can be reduced to the approach of Sun et al. provided that the GRACE data are noise-free and noise in oceanic data is white. Subsequently, we evaluate the quality of the resulting degree-1 and C 20 coefficients by estimating mass anomaly time-series within carefully selected validation areas, where mass transport is small. Our validation shows that, compared to selected Satellite Laser Ranging (SLR) and joint inversion degree-1 solutions, the proposed combination approach better complements GRACE solutions. The annual amplitude of the SLR-based C 10 is probably overestimated by about 1 mm. The performance of the C 20 coefficients, on the other hand, is similar to that of traditionally used solution from the SLR technique.

show abstract

“…Altimetry measurement errors are assumed to be 1.7 cm according to the Topex/Poseidon 10-day orbit errors [24]. In our analysis, the estimates of the sea level anomaly are 3.7 cm on average ( Figure 3), which is closer to the point RMS errors of about 3 cm in [31].…”

Section: A Priori Constraintmentioning

confidence: 56%

Global Surface Mass Variations from Continuous GPS Observations and Satellite Altimetry Data

Zhang

Jin

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The Gravity Recovery and Climate Experiment (GRACE) mission is able to observe the global large-scale mass and water cycle for the first time with unprecedented spatial and temporal resolution. However, no other time-varying gravity fields validate GRACE. Furthermore, the C 20 of GRACE is poor, and no GRACE data are available before 2002 and there will likely be a gap between the GRACE and GRACE-FOLLOW-ON mission. To compensate for GRACE's shortcomings, in this paper, we provide an alternative way to invert Earth's time-varying gravity field, using a priori degree variance as a constraint on amplitudes of Stoke's coefficients up to degree and order 60, by combining continuous GPS coordinate time series and satellite altimetry (SA) mean sea level anomaly data from January 2003 to December 2012. Analysis results show that our estimated zonal low-degree gravity coefficients agree well with those of GRACE, and large-scale mass distributions are also investigated and assessed. It was clear that our method effectively detected global large-scale mass changes, which is consistent with GRACE observations and the GLDAS model, revealing the minimums of annual water cycle in the Amazon in September and October. The global mean mass uncertainty of our solution is about two times larger than that of GRACE after applying a Gaussian spatial filter with a half wavelength at 500 km. The sensitivity analysis further shows that ground GPS observations dominate the lower-degree coefficients but fail to contribute to the higher-degree coefficients, while SA plays a complementary role at higher-degree coefficients. Consequently, a comparison in both the spherical harmonic and geographic domain confirms our global inversion for the time-varying gravity field from GPS and Satellite Altimetry.

show abstract

Seasonal and interannual global surface mass variations from multisatellite geodetic data

Cited by 82 publications

References 48 publications

Nontidal ocean loading: amplitudes and potential effects in GPS height time series

Nontidal ocean loading: amplitudes and potential effects in GPS height time series

Statistically optimal estimation of degree-1 and C20 coefficients based on GRACE data and an ocean bottom pressure model

Global Surface Mass Variations from Continuous GPS Observations and Satellite Altimetry Data

Contact Info

Product

Resources

About