Mass evolution of Mediterranean, Black, Red, and Caspian Seas from GRACE and altimetry: accuracy assessment and solution calibration
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- 作者：B. D. Loomis ; S. B. Luthcke
- 关键词：GRACE ; Sea surface altimetry ; Steric sea level ; Inter ; satellite range ; acceleration ; EEMD
- 刊名：Journal of Geodesy
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：91
- 期：2
- 页码：195-206
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Geophysics/Geodesy; Earth Sciences, general;
- 出版者：Springer Berlin Heidelberg
- ISSN：1432-1394
- 卷排序：91
文摘
We present new measurements of mass evolution for the Mediterranean, Black, Red, and Caspian Seas as determined by the NASA Goddard Space Flight Center (GSFC) GRACE time-variable global gravity mascon solutions. These new solutions are compared to sea surface altimetry measurements of sea level anomalies with steric corrections applied. To assess their accuracy, the GRACE- and altimetry-derived solutions are applied to the set of forward models used by GSFC for processing the GRACE Level-1B datasets, with the resulting inter-satellite range-acceleration residuals providing a useful metric for analyzing solution quality. We also present a differential correction strategy to calibrate the time series of mass change for each of the seas by establishing the strong linear relationship between differences in the forward modeled mass and the corresponding range-acceleration residuals between the two solutions. These calibrated time series of mass change are directly determined from the range-acceleration residuals, effectively providing regionally-tuned GRACE solutions without the need to form and invert normal equations. Finally, the calibrated GRACE time series are discussed and combined with the steric-corrected sea level anomalies to provide new measurements of the unmodeled steric variability for each of the seas over the span of the GRACE observation record. We apply ensemble empirical mode decomposition (EEMD) to adaptively sort the mass and steric components of sea level anomalies into seasonal, non-seasonal, and long-term temporal scales.