Combination of GRACE monthly gravity field solutions from different processing strategies

Jean, Yoomin; Meyer, Ulrich; Jäggi, Adrian

doi:10.1007/s00190-018-1123-5

Cited by 39 publications

(47 citation statements)

References 39 publications

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“…We derive VCE weights in order to produce the combined GFMs from the individual GFMs produced at AIUB, ASU, IfG and OSU. The VCE weights are derived on the solution level according to Jean et al (2018), considering the individual models up to degree 20 only; if this is not done, the extremely high noise at the degrees close to 40 (the maximum degree of the individual solutions) dominates the estimation of the weights, which leads to a slightly worse agreement with GRACE (Teixeira da Encarnação and ). Irrespective of this, the maximum degree of the combined models is the same as the individual models (degree 40).…”

Section: Combinationmentioning

confidence: 99%

Multi-approach gravity field models from Swarm GPS data

Encarnação

Visser

Arnold

et al. 2019

Preprint

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Abstract. Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE and GOCE satellite missions, the geophysical community has identified the need for the continued monitoring of the time-variable component with the purpose of estimating the hydrological and glaciological yearly cycles and long-term trends. Currently, the GRACE-FO satellites are the sole dedicated provider of these data, while previously the GRACE mission fulfilled that role for 15 years. There is a data gap spanning from July 2017 to May 2018 between the end of the GRACE mission and start the of GRACE-FO, while the Swarm satellites have collected gravimetric data with their GPS receivers since December 2013. We present high-quality gravity field models from Swarm data that constitute an alternative and independent source of gravimetric data, which could help alleviate the consequences of the 10-month gap between GRACE and GRACE-FO, as well as the short gaps in the existing GRACE and GRACE-FO monthly time series. The geodetic community has realized that the combination of different gravity field solutions is superior to any individual model and set up a Combination Service of Time-variable Gravity Fields (COST-G) under the umbrella of the International Gravity Field Service (IGFS), part of the International Association of Geodesy (IAG). We exploit this fact and deliver to the highest quality monthly-independent gravity field models, resulting from the combination of four different gravity field estimation approaches. All solutions are unconstrained and estimated independently from month to month. We tested the added value of including Kinematic Baselines (KBs) in our estimation of Gravity Field Models (GFMs) and conclude that there is no significant improvement. The non-gravitational accelerations measured by the accelerometer on-board Swarm-C were also included in our processing to determine if this would improve the quality of the GFMs, but observed that is only the case when the amplitude of the non-gravitational accelerations is higher than during the current quiet period in solar activity. Using GRACE data for comparison, we demonstrate that the geophysical signal in the Swarm gravity field models is largely restricted to Spherical Harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations of Earth’s gravity field over land areas since mid-2015 with roughly 4 cm Equivalent Water Height (EWH) agreement with respect to a GRACE-derived parametric model. Over ocean areas, we illustrate that a more intense smoothing with 3000 km radius is necessary to resolve large scale gravity variations, which agree with the aforementioned parametric model under 2 cm EWH, while at these spatial scales the model represents variations with amplitudes between 2 and 3.5 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analyses is 1.19 cm, 0.60 cm/year and 0.75 in terms of temporal mean, trend and correlation coefficient, respectively.

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Section: Combinationmentioning

confidence: 99%

Multi-approach gravity field models from Swarm GPS data

Encarnação

Visser

Arnold

et al. 2019

Preprint

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“…A widely used metric to quantify the noise level in GRACE solutions is to look at variability over the oceans, as most of the signal in these regions is expected to be removed through ocean tide and dealiasing models (Bonin et al 2012;Meyer et al 2016;Jean et al 2018). In order to minimize residual signal in the noise estimates, we exclude coastal regions and regions where non-tidal ocean signal is expected, like the Arctic Ocean, Antarctic Circumpolar Current, and the Malvinas confluence.…”

Section: Evaluation Of Estimated Solutionsmentioning

confidence: 99%

GRACE gravity field recovery with background model uncertainties

Kvas

Mayer‐Gürr

2019

J Geod

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In this article, we present a computationally efficient method to incorporate background model uncertainties into the gravity field recovery process. While the geophysical models typically used during the processing of GRACE data, such as the atmosphere and ocean dealiasing product, have been greatly improved over the last years, they are still a limiting factor of the overall solution quality. Our idea is to use information about the uncertainty of these models to find a more appropriate stochastic model for the GRACE observations within the least squares adjustment, thus potentially improving the gravity field estimates. We used the ESA Earth System Model to derive uncertainty estimates for the atmosphere and ocean dealiasing product in the form of an autoregressive model. To assess our approach, we computed time series of monthly GRACE solutions from L1B data in the time span of 2005 to 2010 with and without the derived error model. Intercomparisons between these time series show that noise is reduced on all spatial scales, with up to 25% RMS reduction for Gaussian filter radii from 250 to 300 km, while preserving the monthly signal. We further observe a better agreement between formal and empirical errors, which supports our conclusion that used uncertainty information does improve the stochastic description of the GRACE observables.

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“…The combination is represented, under the above mentioned simplifications, by the following formulas (Jean et al, 2018):…”

Section: Combination Strategymentioning

confidence: 99%

Combination of precise orbit solutions for Sentinel-3A using Variance Component Estimation

2019

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Abstract. In this article we analyze the benefit of computing a combined solution from individual orbit solutions for the low Earth orbiting satellite Sentinel-3A. The selected combination scheme for calculating the combined solution is Variance Component Estimation (VCE). Before a combination is calculated, the individual orbit solutions are analyzed to identify systematic differences and characteristics. Simulation studies are performed to show under which conditions a combined solution of superior quality may be expected. The combined solution is validated by Satellite Laser Ranging (SLR) measurements and compared with SLR validations of the individual solutions. The result of this investigation shows that VCE, implemented as an iterative procedure, is suitable to obtain an orbit solution of superior quality for Sentinel-3A.

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Combination of GRACE monthly gravity field solutions from different processing strategies

Cited by 39 publications

References 39 publications

Multi-approach gravity field models from Swarm GPS data

Multi-approach gravity field models from Swarm GPS data

GRACE gravity field recovery with background model uncertainties

Combination of precise orbit solutions for Sentinel-3A using Variance Component Estimation

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