Pavel Ditmar

Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

(1) Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands (n.tangdamrongsub@tudelft.nl), (2) Water Resources Management, Delft University of Technology, Delft, The Netherlands, (3) School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, The United States, (4) Operational Water Management, Deltares, Delft, The Netherlands, (5) Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, The Netherlands

Using Satellite Constellations for Improved Determination of Earth's Time-Variable Gravity

The spatiotemporal resolution of the time-variable gravity field models derived from current dedicated gravity field missions is inherently limited by their ground-track coverage. Furthermore, the results are subject to aliasing effects caused by submonthly mass transport signals, such as those caused by atmospheric and ocean processes. To address these issues, this study explores the feasibility of using nondedicated satellite constellations, such as those from commercial communication networks or a low-cost array of custom-built microsatellites, as a complementary data source. The positioning receivers onboard the constellation’s satellites would ideally provide a high density of observations in the form of derived accelerations that, while much less accurate than those obtained from dedicated gravitymissions, are still sufficient to observe the longest wavelength gravity signals at even subdaily intervals. Using a series of simulated mission scenarios, as well as a limited amount of real-data analysis, it is shown that such constellations, acting either independently or when combined with dedicated gravity field missions, may offer a noticeable improvement in the recovery of the large-scale (greater than 1000 km) high-frequency (less than 1month) components of the global gravity field.

Optimizing estimates of annual variations and trends in geocenter motion and J2 from a combination of GRACE data and geophysical models

The focus of the study is optimizing the technique for estimating geocenter motion and variations in J2 by combining data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with output from an Ocean Bottom Pressure (OBP) model and a Glacial Isostatic Adjustment (GIA) model [Swenson et al., 2008; Sun et al., 2016]. First, we conduct an end-to-end numerical simulation study. We generate input time-variable gravity field observations by perturbing a synthetic Earth model with realistically simulated errors. We show that it is important to avoid large errors at short wavelengths and signal leakage from land to ocean, as well as to account for self-attraction and loading (SAL) effects. Second, the optimal implementation strategy is applied to real GRACE data. We show that the estimates of annual amplitude in geocenter motion are in line with estimates from other techniques, such as SLR and global GPS inversion. At the same time, annual amplitudes of C10 and C11 are increased by about 50% and 20%, respectively, compared to estimates based on [Swenson et al., 2008]. Estimates of J2 variations are by about 15% larger than SLR results in terms of annual amplitude. Linear trend estimates are dependent on the adopted GIA model, but still comparable to some SLR results.

Observed changes in the Earth’s dynamic oblateness from GRACE data and geophysical models

A new methodology is proposed to estimate changes in the Earth’s dynamic oblateness (

Gravimetric monitoring of water influx into a gas reservoir: A numerical study based on the ensemble kalman filter

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Comparison of regional and gobal GRACE gravity field models at high latitudes

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Statistically optimal estimation of Greenland Ice Sheet mass variations from GRACE monthly solutions using an improved mascon approach

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