K.-W. Seo

Terrestrial water mass load changes from Gravity Recovery and Climate Experiment (GRACE)

Recent studies show that data from the Gravity Recovery and Climate Experiment (GRACE) is promising for basin- to global-scale water cycle research. This study provides varied assessments of errors associated with GRACE water storage estimates. Thirteen monthly GRACE gravity solutions from August 2002 to December 2004 are examined, along with synthesized GRACE gravity fields for the same period that incorporate simulated errors. The synthetic GRACE fields are calculated using numerical climate models and GRACE internal error estimates. We consider the influence of measurement noise, spatial leakage error, and atmospheric and ocean dealiasing (AOD) model error as the major contributors to the error budget. Leakage error arises from the limited range of GRACE spherical harmonics not corrupted by noise. AOD model error is due to imperfect correction for atmosphere and ocean mass redistribution applied during GRACE processing. Four methods of forming water storage estimates from GRACE spherical harmonics (four different basin filters) are applied to both GRACE and synthetic data. Two basin filters use Gaussian smoothing, and the other two are dynamic basin filters which use knowledge of geographical locations where water storage variations are expected. Global maps of measurement noise, leakage error, and AOD model errors are estimated for each basin filter. Dynamic basin filters yield the smallest errors and highest signal-to-noise ratio. Within 12 selected basins, GRACE and synthetic data show similar amplitudes of water storage change. Using 53 river basins, covering most of Earths land surface excluding Antarctica and Greenland, we document how error changes with basin size, latitude, and shape. Leakage error is most affected by basin size and latitude, and AOD model error is most dependent on basin latitude.

Estimating GRACE Aliasing Errors

Synthetic GRACE aliasing errors are examined for land, ocean and atmospheric signals. Simulated GRACE data are generated using least square fits of spherical harmonics to potential differences. Mass variations are produced by GLDAS (Global Land Data Assimilation Scheme), ECCO (Estimating the Circulation and Climate of Ocean), and the difference between NCEP and ECMWF (as an estimate of dealiasing error). Additional studies include single spherical harmonics and isolated (Gaussian) loads. Aliasing errors are measured by their degree-order spectrum, and arise from inadequate space-time sampling. Because atmospheric variations are rapid, they cause large aliasing errors at low degree, below 15. To better understand these, we examine aliasing due to sinusoidally oscillating single harmonics, and use Kaula’s resonance solution to interpret the aliases. Single temporal frequency Gaussian mass load variations illustrate aliasing variations associated with geographical location.

Filters to estimate water storage variations from GRACE

We examine the problem of obtaining average surface mass load changes in a local area (for example, water content in a river basin) from time-variable Stokes coefficients available from the GRACE mission. A basin function is unity inside a defined geographical region, and zero outside, and can be represented exactly only if Stokes Coefficients of all degrees and orders are available. GRACE Stokes coefficients will contain errors that generally increase with degree, and will be of limited degree range, perhaps to 100 or so. Load variations within a basin should be estimated by minimizing some quantity that accounts for both GRACE measurement error, and leakage error, associated with a finite degree range. To understand this problem, we use Fourier series, the 1-D equivalent to spherical harmonics. The solution of this problem is not unique, and we examine several different approaches. We use Monte Carlo experiments to test the performance of various methods derived. Time series of gridded soil moisture, snow, ocean bottom pressure, atmospheric surface pressure and Gaussian random noise are utilized in an experiment to recover load variations within the Nile basin.