Continental Water Storage Changes from GRACE Line-of-Sight Range Acceleration Measurements

Chen, Y.; Schaffrin, Burkhard; Shum, C. K.

doi:10.1007/978-3-540-74584-6_10

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Finally, derived range-accelerations could also be used as input, provided that the classical acceleration approach is exploited (Rummel 1979). A successful application of this method has been presented by Chen et al (2008a).…”

Section: Computation Of Unconstrained Solutionsmentioning

confidence: 99%

DEOS Mass Transport model (DMT-1) based on GRACE satellite data: methodology and validation

Liu¹,

Ditmar²,

Siemes³

et al. 2010

Geophysical Journal International

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S U M M A R YThe DEOS Mass Transport release 1 (DMT-1) model has been produced on the basis of intersatellite K-band ranging data acquired by the GRACE satellite mission. The functional model exploited in the data processing can be considered as a variant of the acceleration approach. Each element of the data vector is defined as a linear combination of three successive range measurements and can be interpreted as the line-of-sight projection of a weighted average of intersatellite accelerations. As such, the data vector can be directly linked to parameters of the gravitational field. In this way, a series of unconstrained monthly gravity field solutions is produced, each of which is defined as a set of spherical harmonic coefficients complete to degree 120. At the post-processing stage, the unconstrained solutions are filtered with a statistically optimal Wiener-type filter based on full covariance matrices of noise and signal. As such, the DMT-1 model is free from along-track artefacts, which are typical for many other GRACE gravity models. The accuracy of the DMT-1 model has been analysed in different ways. First, the signals observed in areas with minimal mass variations (Sahara, East Antarctica and the middle of the Pacific Ocean) are analysed and interpreted as an upper bound of the noise in the DMT-1 model. It is concluded that the pointwise errors after filtering are of the order of 2-3 cm in terms of equivalent water heights. For the mean mass variations in an area of 10 6 km 2 , the corresponding error reduces to 1.5-2 cm. Second, a time-series of mass variations in the Marie Byrd Land (Antarctica) has been analysed, where the true signal (mostly caused by postglacial rebound) is expected to be close to a linear trend. The rms of the post-fit residuals is found to be 3.3 cm, which is consistent with the error analysis in areas with minimal mass variations. Thirdly, the DMT-1 model has been applied to estimate mass variations in [2003][2004][2005][2006] in Lake Victoria (Africa), where a large drop of water level is observed in recent years. The obtained linear trend (−31 ± 3 cm yr −1 ) is in good agreement with that derived from the satellite altimetry data (−35 ± 1 cm yr −1 ).

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Section: Computation Of Unconstrained Solutionsmentioning

confidence: 99%

DEOS Mass Transport model (DMT-1) based on GRACE satellite data: methodology and validation

Liu¹,

Ditmar²,

Siemes³

et al. 2010

Geophysical Journal International

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show abstract

“…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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“…On the other hand, in the LGD assimilation, the alternative transition matrix ( H ′) is computed by matrix multiplication of H with the Green's function matrix (see, e.g., Y. Chen et al., 2008; Ghobadi‐Far et al., 2022) as follows,

{\mathbf{H}}^{\prime }=\mathbf{A}\mathbf{H},

where A is the Green's function matrix. In the newly proposed assimilation case, model state variables are multiplied by H ′ instead of H .…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, in the LGD assimilation, the alternative transition matrix (H′) is computed by matrix multiplication of H with the Green's function matrix (see, e.g., Y. Chen et al, 2008;Ghobadi-Far et al, 2022) as follows,…”

Section: Methodsmentioning

confidence: 99%

Assimilation of GRACE Follow‐On Inter‐Satellite Laser Ranging Measurements Into Land Surface Models

Khaki

Han

Ghobadi‐Far

et al. 2023

Water Resources Research

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The monthly mean level‐2 (L2) time‐variable gravity as well as level‐3 (L3) Mass Concentration blocks (mascons) data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE‐FO) missions are frequently used for improving land surface models. The conventional data assimilation approach requires several pre‐processing steps like filtering (to suppress the random and systematic errors), correction (to reduce the leakage effect) and scaling (to un‐do the attenuation effect by filters) before integrating the data with the models, for example, when computing total water storage (TWS) changes from L2 data. Moreover, due to the monthly sampling of L2 and L3 data, the model estimates are updated only once a month. This confines the applications of the approaches to cases where water storage is slowly varying at seasonal or interannual time‐scales. We present a new methodology based on direct assimilation of along‐orbit Line‐of‐sight Gravity Difference (LGD) measurements from the GRACE‐FO laser ranging interferometer (LRI) which overcomes the limitation of the conventional approach. Inter‐satellite ranging data reflect the gravitational changes at satellite altitude caused by the instantaneous TWS changes at the Earth surface. Therefore, they pose information at wide‐ranging time‐scales from hours to multiple years. The proposed method is applied globally to assimilate LRI data into a land surface model. Evaluation against multiple satellite‐based and ground data shows the superiority of our proposed approach. The new approach also offers better performance in capturing high‐frequency water storage variations imposed by sub‐monthly climatic events due to its higher number of data assimilation cycles within a month.

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Continental Water Storage Changes from GRACE Line-of-Sight Range Acceleration Measurements

Cited by 8 publications

References 9 publications

DEOS Mass Transport model (DMT-1) based on GRACE satellite data: methodology and validation

DEOS Mass Transport model (DMT-1) based on GRACE satellite data: methodology and validation

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

Assimilation of GRACE Follow‐On Inter‐Satellite Laser Ranging Measurements Into Land Surface Models

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