The impact of common versus separate estimation of orbit parameters on GRACE gravity field solutions

Gravity field parameters are usually determined from observations of the GRACE satellite mission together with arc-specific parameters in a generalized orbit determination process. When separating the estimation of gravity field parameters from the determination of the satellites’ orbits, correlations between orbit parameters and gravity field coefficients are ignored and the latter parameters are biased towards the a priori force model. We are thus confronted with a kind of hidden regularization. To decipher the underlying mechanisms, the Celestial Mechanics Approach is complemented by tools to modify the impact of the pseudo-stochastic arc-specific parameters on the normal equations level and to efficiently generate ensembles of solutions. By introducing a time variable a priori model and solving for hourly pseudo-stochastic accelerations, a significant reduction of noisy striping in the monthly solutions can be achieved. Setting up more frequent pseudo-stochastic parameters results in a further reduction of the noise, but also in a notable damping of the observed geophysical signals. To quantify the effect of the a priori model on the monthly solutions, the process of fixing the orbit parameters is replaced by an equivalent introduction of special pseudo-observations, i.e., by explicit regularization. The contribution of the thereby introduced a priori information is determined by a contribution analysis. The presented mechanism is valid universally. It may be used to separate any subset of parameters by pseudo-observations of a special design and to quantify the damage imposed on the solution.

Time Variable Gravity: Contributions of GOCE Satellite Data to Monthly and Bi-monthly GRACE Gravity Estimates

A feasibility study by Pail et al. (Can GOCE help to improve temporal gravity field estimates? In: Ouwehand L (ed) Proceedings of the 4th International GOCE User Workshop, ESA Publication SP-696, 2011b) shows that GOCE (‘Gravity field and steady-state Ocean Circulation Explorer’) satellite gravity gradiometer (SGG) data in combination with GPS derived orbit data (satellite-to-satellite tracking: SST-hl) can be used to stabilize and reduce the striping pattern of a bi-monthly GRACE (‘Gravity Recovery and Climate Experiment’) gravity field estimate. In this study several monthly (and bi-monthly) combinations of GRACE with GOCE SGG and GOCE SST-hl data on the basis of normal equations are investigated. Our aim is to assess the role of the gradients (solely) in the combination and whether already one month of GOCE observations provides sufficient data for having an impact in the combination. The estimation of clean and stable monthly GOCE SGG normal equations at high resolution ( >  d/o 150) is found to be difficult, and the SGG component, solely, does not show significant added value to monthly and bi-monthly GRACE gravity fields. Comparisons of GRACE-only and combined monthly and bi-monthly solutions show that the striping pattern can only be reduced when using both GOCE observation types (SGG, SST-hl), and mainly between d/o 45 and 60.

Grace Hawthorne als Theodora, Rollenbild, London

Signatur des Originals: S 36/F08785

Grace Pedley, Rollenbild, Hüftbild, in: Joan of Arc

Signatur des Originals: S 36/F09498

(Table 1) GRACE-derived mass balance of the Greenland ice sheet 2002-2010

We re-evaluate the Greenland mass balance for the recent period using low-pass Independent Component Analysis (ICA) post-processing of the Level-2 GRACE data (2002-2010) from different official providers (UTCSR, JPL, GFZ) and confirm the present important ice mass loss in the range of -70 and -90 Gt/y of this ice sheet, due to negative contributions of the glaciers on the east coast. We highlight the high interannual variability of mass variations of the Greenland Ice Sheet (GrIS), especially the recent deceleration of ice loss in 2009-2010, once seasonal cycles are robustly removed by Seasonal Trend Loess (STL) decomposition. Interannual variability leads to varying trend estimates depending on the considered time span. Correction of post-glacial rebound effects on ice mass trend estimates represents no more than 8 Gt/y over the whole ice sheet. We also investigate possible climatic causes that can explain these ice mass interannual variations, as strong correlations between GRACE-based mass balance and atmosphere/ocean parallels are established: (1) changes in snow accumulation, and (2) the influence of inputs of warm ocean water that periodically accelerate the calving of glaciers in coastal regions and, feed-back effects of coastal water cooling by fresh currents from glaciers melting. These results suggest that the Greenland mass balance is driven by coastal sea surface temperature at time scales shorter than accumulation.

Global and Regional Sea level budget components from GRACE and radar altimetry (2002-2014)

This dataset contains the result of a joint least squares inversion of GRACE and altimetry data. The results are evaluated in terms of sea level change for the global ocean as well as dedicated areas. In addition, some auxiliary data is provided to enable reproducibility of the results in Rietbroek et al. 2016, and a google Earth kmz file is provided which visualizes the trends derived from the inversion results.

Australian water mass variations from GRACE data linked to Indo-Pacific climate variability

Time-variable gravity data from the Gravity Recovery And Climate Experiment (GRACE) mission are used to study total water content over Australia for the period 2002–2010. A time-varying annual signal explains 61% of the variance of the data, in good agreement with two independent estimates of the same quantity from hydrological models. Water mass content variations across Australia are linked to Pacific and Indian Ocean variability, associated with El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), respectively. From 1989, positive (negative) IOD phases were related to anomalously low (high) precipitation in southeastern Australia, associated with a reduced (enhanced) tropical moisture flux. In particular, the sustained water mass content reduction over central and southern regions of Australia during the period 2006–2008 is associated with three consecutive positive IOD events.

Steric sea-level variations inferred from combined Topex/Poseidon altimetry and GRACE gravimetry

In this study, we propose to estimate the steric sea-level variations over a < 2-year period (April 2002 through December 2003) by combining global mean sea level (GMSL) based on Topex/ Poseidon (T/P) altimetry with time-variable geoid averaged over the oceans, as observed by the GRACE (Gravity Recovery and Climate Experiment) satellite. In effect, altimetry-derived GMSL changes results from two contributions: Steric (thermal plus salinity) effects due to sea water density change and ocean mass change due to water exchange with atmosphere and continents. On the other hand, GRACE data over the oceans provide the ocean mass change component only. The paper first discusses the corrections to apply to the GRACE data. Then the steric contribution to the GMSL is estimated using GRACE and T/P data. Comparison with available thermal expansion based on in situ hydrographic data is performed.

On the decorrelation filtering of RL05 GRACE data for global applications

In autumn 2012, the new release 05 (RL05) of monthly geopotencial spherical harmonics Stokes coefficients (SC) from GRACE (Gravity Recovery and Climate Experiment) mission was published. This release reduces the noise in high degree and order SC, but they still need to be filtered. One of the most common filtering processing is the combination of decorrelation and Gaussian filters. Both of them are parameters dependent and must be tuned by the users. Previous studies have analyzed the parameters choice for the RL05 GRACE data for oceanic applications, and for RL04 data for global application. This study updates the latter for RL05 data extending the statistics analysis. The choice of the parameters of the decorrelation filter has been optimized to: (1) balance the noise reduction and the geophysical signal attenuation produced by the filtering process; (2) minimize the differences between GRACE and model-based data; (3) maximize the ratio of variability between continents and oceans. The Gaussian filter has been optimized following the latter criteria. Besides, an anisotropic filter, the fan filter, has been analyzed as an alternative to the Gauss filter, producing better statistics.