A high resolution model of linear trend in mass variations from DMT-2
Added value of accounting for coloured noise in GRACE data
H. Farahani (TU Delft - Physical and Space Geodesy)
Pavel Ditmar (TU Delft - Physical and Space Geodesy)
P. Miragaia Gomes Inacio (TU Delft - Physical and Space Geodesy)
Olga Engels (TU Delft - Physical and Space Geodesy)
B. C. Gunter (TU Delft - Physical and Space Geodesy, Georgia Institute of Technology)
Roland Klees (TU Delft - Physical and Space Geodesy)
X. Guo (TU Delft - Physical and Space Geodesy, Wuhan University)
Jing Guo (Wuhan University)
Y. Sun (TU Delft - Physical and Space Geodesy)
Xianglin Liu (Fugro Intersite B.V.)
Qile Zhao (Wuhan University)
REM Riva (TU Delft - Physical and Space Geodesy)
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Abstract
We present a high resolution model of the linear trend in the Earth’s mass variations based on DMT-2 (Delft Mass Transport model, release 2). DMT-2 was produced primarily from K-Band Ranging (KBR) data of the Gravity Recovery And Climate Experiment (GRACE). It comprises a time series of monthly solutions complete to spherical harmonic degree 120. A novel feature in its production was the accurate computation and incorporation of stochastic properties of coloured noise when processing KBR data. The unconstrained DMT-2 monthly solutions are used to estimate the linear trend together with a bias, as well as annual and semi-annual sinusoidal terms. The linear term is further processed with an anisotropic Wiener filter, which uses full noise and signal covariance matrices. Given the fact that noise in an unconstrained model of the trend is reduced substantially as compared to monthly solutions, the Wiener filter associated with the trend is much less aggressive compared to a Wiener filter applied to monthly solutions. Consequently, the trend estimate shows an enhanced spatial resolution. It allows signals in relatively small water bodies, such as Aral sea and Ladoga lake, to be detected. Over the ice sheets, it allows for a clear identification of signals associated with some outlet glaciers or their groups. We compare the obtained trend estimate with the ones from the CSR-RL05 model using (i) the same approach based on monthly noise covariance matrices and (ii) a commonly-used approach based on the DDK-filtered monthly solutions. We use satellite altimetry data as independent control data. The comparison demonstrates a high spatial resolution of the DMT-2 linear trend. We link this to the usage of high-accuracy monthly noise covariance matrices, which is due to an accurate computation and incorporation of coloured noise when processing KBR data. A preliminary comparison of the linear trend based on DMT-2 with that computed from GSFC global mascons v01 reveals, among other, a high concentration of the signal along the coast for both models in areas like the ice sheets, Gulf of Alaska, and Iceland.
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