Accuracy assessment of the monthly GRACE geoids based upon a simulation

Abstract

The purpose of this paper is to demonstrate the effect of geophysical background model errors that affects temporal gravity solutions provided by the Gravity Recovery And Climate Experiment (GRACE). Initial performance estimates by Dickey et al. (1997) suggested a formal geoid RMS error better than 0.1 mm up to spherical harmonic degree 5. Now that the GRACE gravity models and data are available, it is evident that these original expectations were too optimistic. Our hypothesis is that this is partially explained by errors in geophysical background models that need to be applied in the GRACE data reduction, and that this effect was not considered by Dickey et al. (1997). We discuss the results of a closed-loop simulation, where satellite trajectory prediction software is used for the generation of GRACE range-rate data and GRACE orbit solutions with the help of the Global Positioning System (GPS). During the recovery step in our closedloop simulation, we show that simulated nuisance signals (based on tide and air pressure model differences) map to a 0.7 mm geoid effect for periods longer than 3 months and to less than 0.4 mm for periods shorter than 3 months. The long-period geoid hydrology signal is at a level of 4.5 mm, while the short-period hydrology is at 0.25 mm. The long-period ocean bottom pressure (OBP) signal maps at 0.8 mm and for short periods it is 0.4 mm. We conclude that short-period effects are difficult to observe by GRACE and that long-period

Monitoring mass changes in the Volta River basin using GRACE satellite gravity and TRMM precipitation

“…Striping is the largest problem in GRACE solutions and according to Awange et al (2009), there remains some conjecture as to the exact cause of the striping. However, in agreement with Swenson and Wahr (2006) and Schrama and Visser (2006) it is thought to be mostly due to weight being placed on the along-track K-band ranging (KBR) data coupled with inaccurate de-aliasing models and the mission configuration.…”

Signal and noise in Gravity Recovery and Climate Experiment (GRACE) observed surface mass variations

“…At best such an analysis shows that category 1 errors provided in the GRACE monthly gravity field product are in agreement with synthetic errors extracted from surface mass data. Category 2 errors, or background model errors (BMEs), are more difficult to quantify in the current GRACE product because a simulation of BMEs requires reprocessing of GRACE observation data, preferably twice with different background correction models that take away short periodic variations in the gravity field [see, e.g., Thompson et al , 2004; Schrama and Visser , 2006; Ray and Luthcke , 2006]. One of the conclusions of Schrama and Visser [2006] is that the largest BMEs can be expected from ocean tides and atmospheric pressure effects but that their errors are about an order smaller than the dominating continental hydrology signal in the GRACE data set.…”

“…Category 2 errors, or background model errors (BMEs), are more difficult to quantify in the current GRACE product because a simulation of BMEs requires reprocessing of GRACE observation data, preferably twice with different background correction models that take away short periodic variations in the gravity field [see, e.g., Thompson et al , 2004; Schrama and Visser , 2006; Ray and Luthcke , 2006]. One of the conclusions of Schrama and Visser [2006] is that the largest BMEs can be expected from ocean tides and atmospheric pressure effects but that their errors are about an order smaller than the dominating continental hydrology signal in the GRACE data set. In this paper BMEs are assumed to be small and therefore BMEs are simplified in the sense that we obtain them directly as a difference of two competing background models that in turn are based on fundamentally different processing strategies.…”

“…In this paper BMEs are assumed to be small and therefore BMEs are simplified in the sense that we obtain them directly as a difference of two competing background models that in turn are based on fundamentally different processing strategies. We compared the BME spectra for tides shown here to the approaches of both Schrama and Visser [2006] and Ray and Luthcke [2006] to conclude that there are no fundamental difference between these approaches for n < 25.…”

Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models

“…Being a single-orbital plane mission, the ability of GRACE to recover short-time temporal variations in gravity is limited. Schrama and Visser (2007) demonstrated the effect of background model errors on the accuracy of monthly fields based upon closed-loop simulations. In Wahr et al (2006), error estimates were derived from non-annual variability, fitting quite well to published 'calibrated' errors.…”