Impact of Time Variable Gravity on Annual Sea Level Variability from Altimetry

2015 ◽  
pp. 55-62 ◽  
Author(s):  
Saskia Esselborn ◽  
Tilo Schöne ◽  
Sergei Rudenko
Keyword(s):  
Sea Level ◽  
Time Variable ◽  
Sea Level Variability ◽  
Time Variable Gravity ◽  
Variable Gravity

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

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.

Resolving sea level contributions by identifying fingerprints in time-variable gravity and altimetry

2012 ◽  
Vol 59-60 ◽  
pp. 72-81 ◽  
Author(s):  
R. Rietbroek ◽  
S.-E. Brunnabend ◽  
J. Kusche ◽  
J. Schröter
Keyword(s):  
Sea Level ◽  
Time Variable ◽  
Time Variable Gravity ◽  
Variable Gravity

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

Solid Earth ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 323-339 ◽  
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.

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

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.

scholarly journals Earth’s Time-Variable Gravity from GRACE Follow-On K-Band Range-Rates and Pseudo-Observed Orbits

2021 ◽  
Vol 13 (9) ◽  
pp. 1766
Author(s):  
Igor Koch ◽  
Mathias Duwe ◽  
Jakob Flury ◽  
Akbar Shabanloui
Keyword(s):  
Gravity Field ◽  
Time Variable ◽  
Time Variable Gravity ◽  
Gravity Field Recovery ◽  
Variable Gravity ◽  
Earth’S System ◽  
Grace Mission ◽  
Insight Into ◽  
K Band

During its science phase from 2002–2017, the low-low satellite-to-satellite tracking mission Gravity Field Recovery And Climate Experiment (GRACE) provided an insight into Earth’s time-variable gravity (TVG). The unprecedented quality of gravity field solutions from GRACE sensor data improved the understanding of mass changes in Earth’s system considerably. Monthly gravity field solutions as the main products of the GRACE mission, published by several analysis centers (ACs) from Europe, USA and China, became indispensable products for quantifying terrestrial water storage, ice sheet mass balance and sea level change. The successor mission GRACE Follow-On (GRACE-FO) was launched in May 2018 and proceeds observing Earth’s TVG. The Institute of Geodesy (IfE) at Leibniz University Hannover (LUH) is one of the most recent ACs. The purpose of this article is to give a detailed insight into the gravity field recovery processing strategy applied at LUH; to compare the obtained gravity field results to the gravity field solutions of other established ACs; and to compare the GRACE-FO performance to that of the preceding GRACE mission in terms of post-fit residuals. We use the in-house-developed MATLAB-based GRACE-SIGMA software to compute unconstrained solutions based on the generalized orbit determination of 3 h arcs. K-band range-rates (KBRR) and kinematic orbits are used as (pseudo)-observations. A comparison of the obtained solutions to the results of the GRACE-FO Science Data System (SDS) and Combination Service for Time-variable Gravity Fields (COST-G) ACs, reveals a competitive quality of our solutions. While the spectral and spatial noise levels slightly differ, the signal content of the solutions is similar among all ACs. The carried out comparison of GRACE and GRACE-FO KBRR post-fit residuals highlights an improvement of the GRACE-FO K-band ranging system performance. The overall amplitude of GRACE-FO post-fit residuals is about three times smaller, compared to GRACE. GRACE-FO post-fit residuals show less systematics, compared to GRACE. Nevertheless, the power spectral density of GRACE-FO and GRACE post-fit residuals is dominated by similar spikes located at multiples of the orbital and daily frequencies. To our knowledge, the detailed origin of these spikes and their influence on the gravity field recovery quality were not addressed in any study so far and therefore deserve further attention in the future. Presented results are based on 29 monthly gravity field solutions from June 2018 until December 2020. The regularly updated LUH-GRACE-FO-2020 time series of monthly gravity field solutions can be found on the website of the International Centre for Global Earth Models (ICGEM) and in LUH’s research data repository. These operationally published products complement the time series of the already established ACs and allow for a continuous and independent assessment of mass changes in Earth’s system.

scholarly journals Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model

2009 ◽  
Vol 83 (10) ◽  
pp. 903-913 ◽  
Author(s):  
J. Kusche ◽  
R. Schmidt ◽  
S. Petrovic ◽  
R. Rietbroek
Keyword(s):  
Hydrological Model ◽  
Time Variable ◽  
Time Variable Gravity ◽  
Variable Gravity

Combination Service for Time-variable Gravity Fields (COST-G): operational GRACE-FO combination and validation of Chinese GRACE time-series

2021 ◽  
Author(s):  
Ulrich Meyer ◽  
Martin Lasser ◽  
Adrian Jäggi ◽  
Christoph Dahle ◽  
Frank Flechtner ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Assessment ◽  
International Association ◽  
Time Variable ◽  
Operational Mode ◽  
Time Variable Gravity ◽  
Gravity Fields ◽  
Variable Gravity ◽  
Processed Products ◽  
The Cost

<p>The Combination Service for Time-variable Gravity Fields (COST-G) of the International Association of Geodesy (IAG) provides combined monthly gravity fields of its associated and partner Analysis Centers (ACs). In November 2020, the combination of monthly GRACE-FO gravity fields started its operational mode, providing consolidated L2 (spherical harmonics) and L3 (gridded and post- processed) products with a latency of currently 3 months. We present an overview and quality assessment of the available products.</p><p>COST-G aims at the extension of its service to include further GRACE and GRACE-FO analysis centers. In January 2020 a collaboration with representatives of five Chinese ACs was initiated, who provided GRACE time-series according to the COST-G requirements. We present the results of a test combination with the Chinese AC models, including comparison and quality assessment of all contributing time-series and validation of the combined gravity fields.</p>

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