Data-driven gap filling and spatio-temporal filtering of the
GRACE/GRACE-FO records
Abstract
Gravity Recovery And Climate Experiment (GRACE) and GRACE-Follow On
(GRACE-FO) global monthly measurements of Earth’s gravity field have led
to significant advances in the quantification of mass transfer on Earth.
Yet, a long temporal gap between missions prevents interpretation of
long-term mass variations. Moreover, instrumental and processing errors
translate into large non-physical stripes polluting geophysical signals.
We use Multichannel Singular Spectrum Analysis (M-SSA) to overcome both
issues by exploiting spatio-temporal information of multiple Level-2
GRACE/GRACE-FO solutions. We statistically replace missing data and
outliers using iterative M-SSA on Equivalent Water Height (EWH) time
series processed by CSR, GFZ, GRAZ, and JPL to form a combined evenly
spaced solution. Then, M-SSA is applied to retrieve common signals
between each EWH time series and its neighbours to reduce residual
spatially uncorrelated noise. We develop a complementary filter, based
on the residual noise between fully processed data and a parametric fit
to observations, to further reduce persisting stripes. Comparing
GRACE/GRACE-FO M-SSA solution with SLR low-degree Earth’s gravity field
and hydrological model demonstrates its ability to statistically fill
missing observations. Our solution reaches a noise level comparable to
mass concentration (mascon) solutions over oceans, without requiring
\textit{a priori} information or regularisation. While
short-wavelength signals are hampered by filtering of spherical
harmonics solutions or challenging to capture using mascon solutions, we
show that our technique efficiently recovers localized mass variations
using well-documented mass transfers associated with reservoir
impoundments.