GRACE time-variable gravity field recovery using an improved energy balance approach

Shang, Ke; Guo, Junyi; Shum, C. K.; Dai, Chunli; Luo, Jia

doi:10.1093/gji/ggv392

Cited by 24 publications

(14 citation statements)

References 37 publications

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“…The main development of the IEBA, used at Ohio State University (OSU), concerns the handling of the noise in the kinematic position and the weighting of the potential observations. Unlike the application of this approach to GRACE ll-SST data by Shang et al (2015), the term related to the Earth's rotation cannot be neglected in the processing of hl-SST data. From the kinematic positions, the velocity is derived with a 61 data points, sliding window, quadratic polynomial filter similar to Bezděk et al (2014).…”

Section: Improved Energy Balance Approachmentioning

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: Improved Energy Balance Approachmentioning

confidence: 99%

Multi-approach gravity field models from Swarm GPS data

Encarnação

Visser

Arnold

et al. 2019

Preprint

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“…It is a continuation of the activities described in Teixeira da , which included the production of gravity field solutions using three different methods, referred to as the celestial mechanics approach (CMA) (Beutler et al, 2010), decorrelated acceleration approach (DAA) (Bezdek et al, 2014), and short-arc approach (SAA) (Mayer-Gürr, 2006). In this work, a fourth method, referred to as the improved energy balance approach (IEBA) (Shang et al, 2015), is added. The combination of the four gravity field solutions into combined models will be more advanced than in Teixeira da , where a straightforward averaging was applied.…”

Section: Introductionmentioning

confidence: 99%

Description of the multi-approach gravity field models from Swarm GPS data

Encarnação¹,

Visser

Arnold

et al. 2020

Earth Syst. Sci. Data

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Abstract. Although the knowledge of the gravity of the Earth has improved considerably with CHAMP, GRACE, and GOCE (see appendices for a list of abbreviations) 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 (GFMs) 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 the 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 the highest-quality monthly GFMs, 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 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 we 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 GFMs is largely restricted to spherical harmonic degrees below 12. A 750 km smoothing radius is suitable to retrieve the temporal variations in Earth's gravity field over land areas since mid-2015 with roughly 4 cm equivalent water height (EWH) agreement with respect to GRACE. 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 GRACE roughly at the level of 1 cm EWH, while at these spatial scales the GRACE observes variations with amplitudes between 0.3 and 1 cm EWH. The agreement with GRACE and GRACE-FO over nine selected large basins under analysis is 0.91 cm, 0.76 cm yr−1, and 0.79 in terms of temporal mean, trend, and correlation coefficient, respectively. The Swarm monthly models are distributed on a quarterly basis at ESA's Earth Swarm Data Access (at https://swarm-diss.eo.esa.int/, last access: 5 June 2020, follow Level2longterm and then EGF) and at the International Centre for Global Earth Models (http://icgem.gfz-potsdam.de/series/02_COST-G/Swarm, last access: 5 June 2020), as well as identified with the DOI https://doi.org/10.5880/ICGEM.2019.006 (Encarnacao et al., 2019).

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“…These approaches have been used to study regional and global time-variable gravity, continental hydrology, tides, etc. (e.g., Chen et al, 2008;Han et al, 2005;Killett et al, 2011;Ramillien et al, 2011;Shang et al, 2015;Tangdamrongsub et al, 2012;Watkins et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Transfer Function Between Line‐of‐Sight Gravity Difference and GRACE Intersatellite Ranging Data and an Application to Hydrological Surface Mass Variation

et al. 2018

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We develop a transfer function to determine in situ line‐of‐sight gravity difference (LGD) directly from Gravity Recovery and Climate Experiment (GRACE) range‐acceleration measurements. We first reduce GRACE data to form residual range‐acceleration referenced to dynamic orbit computed with a reference gravity field and nonconservative force data. Thus, the residuals and the corresponding LGD data reflect time‐variable gravity signals. A transfer function is designed based on correlation‐admittance spectral analysis. The correlation spectrum shows that residual range‐acceleration and LGD are near‐perfectly correlated for frequencies >5 cycles‐per‐revolution. The admittance spectrum quantifies that the LGD response to range‐acceleration is systematically larger at lower frequencies, due to the increased contribution of centrifugal acceleration. We find that the correlation and admittance spectra are stationary (i.e., are independent of time, satellite altitude, and gravity strength) and, therefore, can be determined a priori with high fidelity. We determine the spectral transfer function and the equivalent time domain filter. Using both synthetic and actual GRACE data, we demonstrate that in situ LGD can be estimated via the transfer function with an estimation error of 0.15 nm/s2, whereas the actual GRACE data error is around 1.0 nm/s2. We present an application of LGD data to surface water storage changes in large basins such as Amazon, Congo, Parana, and Mississippi by processing 11 years of GRACE data. Runoff routing models are calibrated directly using LGD data. Our technique demonstrates a new way of using GRACE data by forward modeling of various geophysical models and in‐orbit comparison with such GRACE in situ data.

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GRACE time-variable gravity field recovery using an improved energy balance approach

Cited by 24 publications

References 37 publications

Multi-approach gravity field models from Swarm GPS data

Multi-approach gravity field models from Swarm GPS data

Description of the multi-approach gravity field models from Swarm GPS data

A Transfer Function Between Line‐of‐Sight Gravity Difference and GRACE Intersatellite Ranging Data and an Application to Hydrological Surface Mass Variation

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