References of "Weigelt, Matthias 50003312"
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See detailA warmer world
van Dam, Tonie UL; Weigelt, Matthias UL; Jäggi, Adrian

in Pan European Networks: Science & Technology (2015), (14), 58-59

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See detailHow well can the combination of hlSST and SLR replace GRACE? A discussion from the point of view of applications
Weigelt, Matthias UL; van Dam, Tonie UL; Baur, Oliver et al

Scientific Conference (2014, September 30)

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See detailTime varying gravity from SLR and combined SLR and high-low satellite-to-satellite tracking data
Sośnica, Krzysztof; Jäggi, Adrian; Weigelt, Matthias UL et al

Scientific Conference (2014, September 30)

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See detailSeasonal Variations of Low-degree Spherical Harmonics Derived from GPS Data and Loading Models
Wei, Na UL; van Dam, Tonie UL; Weigelt, Matthias UL et al

Scientific Conference (2014, September 30)

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See detailA methodology to choose the orbit for a double-pair-scenario future gravity satellite mission: Experiences from the SC4MGV project
Weigelt, Matthias UL; Iran Pour, Siavash; Murböck, Michael et al

Scientific Conference (2014, September 30)

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See detailGOCE Precise Science Orbits for the entire mission and their use for Gravity Field Recovery
Jäggi, Adrian; Bock, Heike; Meyer, Ulrich et al

Scientific Conference (2014, August)

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See detailGenetic-algorithm based search strategy for optimal scenarios of future dual-pair gravity satellite missions
Iran Pour, Siavash; Reubelt, Tilo; Weigelt, Matthias UL et al

Poster (2014, June)

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See detailTowards combined global monthly gravity field solutions
Jäggi, Adrian; Meyer, Ulrich; Weigelt, Matthias UL et al

Scientific Conference (2014, April)

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See detailOn the capability of Swarm for surface mass variation monitoring: Quantitative assessment based on orbit information from CHAMP, GRACE and GOCE
Baur, Oliver; Weigelt, Matthias UL; Zehentner, Norbert et al

Scientific Conference (2014, April)

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See detailSingular spectrum analysis for modeling seasonal signals from GPS time series
Chen, Qiang; van Dam, Tonie UL; Sneeuw, Nico et al

in Journal of Geodynamics (2013), 72

Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals quasi-annual ... [more ▼]

Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals quasi-annual and semi-annual signals are modeled by least-squares fitting harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they will have a time-variable amplitude and phase. Recently, Davis et al.(2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. Singular Spectrum Analysis (SSA) is a non-parametric method, which uses time domain data to extract information from short and noisy time series without a priori knowledge of the dynamics affecting the time series. A prominent benefit is that trends obtained in this way are not necessarily linear. Further, true oscillations can be amplitude and phase modulated. In this work, we will assess the value of SSA for extracting time-variable seasonal signals from GPS time series. We compare our SSA-based results to those obtained using 1) least-squares analysis and 2) Kalman filtering. Our results demonstrate that SSA is a viable and complementary tool for extracting modulated oscillations from GPS time series. [less ▲]

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See detailOn the capability to derive mass estimates from high-low satellite-to-satellite tracking data
Weigelt, Matthias UL; van Dam, Tonie UL; Tourian, M. J. et al

Poster (2013, December)

Recently it has been shown that it is possible to derive time-variable gravity signals from high-low satellite-to-satellite tracking (hl-SST) missions (Weigelt et al. 2013, JGR:Solid Earth, doi:10.1002 ... [more ▼]

Recently it has been shown that it is possible to derive time-variable gravity signals from high-low satellite-to-satellite tracking (hl-SST) missions (Weigelt et al. 2013, JGR:Solid Earth, doi:10.1002/jgrb.50283). Based on the GPS information only, we will present results derived from the dedicated gravity field missions CHAMP, GRACE and GOCE which allow us to determine mass estimates for various applications. Hydrologically induced mass changes on land cause the strongest mass variations in the gravity field and can be easily identified in the hl-SST data, especially in areas with strong signals such as the Amazon basin. Ice melt in Greenland can be derived from the data and mass estimates compare well to corresponding GRACE estimates. Also, loading time series based on these gravity field solutions agree well with GPS observations for various stations around the globe. We also discuss the limitations of the data, e.g. in detecting signals related to glacial isostatic adjustment or earthquake-induced gravity field changes. Overall, we will demonstrate that the quality of the GPS data is sufficient nowadays and with a proper processing strategy it is possible to derive reasonable mass estimates. As such, this type of observations may allow to bridge a possible gap between GRACE and its successor GRACE Follow-On scheduled for launch in 2017. [less ▲]

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See detailTowards combined global monthly gravity field solutions
Jäggi, A.; Meyer, U.; Beutler, G. et al

Scientific Conference (2013, October)

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See detailAn assessment of degree-2 Stokes coefficients from Earth rotation data
Meyrath, Thierry UL; van Dam, Tonie UL; Weigelt, Matthias UL et al

in Geophysical Journal International (2013), 195((1)), 249-259

Variations in the degree-2 Stokes coefficients C20, C21 and S21 can be used to understand long and short-term climate forcing. Here, we derive changes in these coefficients for the period 2003 ... [more ▼]

Variations in the degree-2 Stokes coefficients C20, C21 and S21 can be used to understand long and short-term climate forcing. Here, we derive changes in these coefficients for the period 2003 January–2012 April using Earth rotation data. Earth rotation data contain contributions from motion terms (the effects of winds and currents) and contributions from the effects of mass redistribution. We remove the effects of tides, atmospheric winds and oceanic currents from our data. We compare two different models of atmospheric and oceanic angular momentum for removing the effects of winds and currents: (1) using products from the National Centers for Environmental Prediction and (2) using data from the European Centre for Medium-range Weather Forecasts (ECMWF). We assess the quality of these motion models by comparing the two resulting sets of degree-2 Stokes coefficients to independent degree-2 estimates from satellite laser ranging (SLR), GRACE and a geophysical loading model. We find a good agreement between the coefficients from Earth rotation and the coefficients from other sources. In general, the agreement is better for the coefficients we obtain by removing winds and currents effects using the ECMWF model. In this case, we find higher correlations with the independent models and smaller scatters in differences. This fact holds in particular for C20 and C21, whereas we cannot observe a significant difference for S21. At the annual and semiannual periods, our Earth rotation derived coefficients agree well with the estimates from the other sources, particularly for C21 and S21. The slight discrepancies we obtain for C20 can probably be explained by errors in the atmospheric models and are most likely the result of an over-/underestimation of the annual and semiannual contributions of atmospheric winds to the length-of-day excitation. [less ▲]

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See detailBridging the gap between GRACE and GRACE follow-on: the potential of SWARM and SLR to detect time-variable gravity
Baur, O.; Reubelt, T.; Weigelt, Matthias UL

Scientific Conference (2013, September)

Detailed reference viewed: 81 (0 UL)
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See detailOn the capability of non-dedicated GPS-tracked satellite constellations for estimating mass variations: case study SWARM
Reubelt, T.; Baur, O.; Weigelt, Matthias UL et al

Scientific Conference (2013, September)

Detailed reference viewed: 50 (3 UL)