Thu Apr 10, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Sun, Qin, Xie, ZhengHui, and Tian, XiangJun, 2015. GRACE terrestrial water storage data assimilation based on the ensemble four-dimensional variational method PODEn4DVar: Method and validation. Science China Earth Sciences, 58(3):371–384, doi:10.1007/s11430-014-4978-1.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2015ScChD..58..371S,
author = {{Sun}, Qin and {Xie}, ZhengHui and {Tian}, XiangJun},
title = "{GRACE terrestrial water storage data assimilation based on the ensemble four-dimensional variational method PODEn4DVar: Method and validation}",
journal = {Science China Earth Sciences},
keywords = {data assimilation, land surface model, terrestrial water storage, ensemble four-dimensional variational data assimilation method},
year = 2015,
month = mar,
volume = {58},
number = {3},
pages = {371-384},
abstract = "{Seasonal and interannual changes in the Earth's gravity field are mainly
due to mass exchange among the atmosphere, ocean, and
continental water sources. The terrestrial water storage
changes, detected as gravity changes by the Gravity Recovery and
Climate Experiment (GRACE) satellites, are mainly caused by
precipitation, evapotranspiration, river transportation and
downward infiltration processes. In this study, a land data
assimilation system LDAS-G was developed to assimilate the GRACE
terrestrial water storage (TWS) data into the Community Land
Model (CLM3.5) using the POD-based ensemble four-dimensional
variational assimilation method PODEn4DVar, disaggregating the
GRACE large-scale terrestrial water storage changes vertically
and in time, and placing constraints on the simulation of
vertical hydrological variables to improve land surface
hydrological simulations. The ideal experiments conducted at a
single point and assimilation experiments carried out over China
by the LDAS-G data assimilation system showed that the system
developed in this study improved the simulation of land surface
hydrological variables, indicating the potential of GRACE data
assimilation in large-scale land surface hydrological research
and applications.}",
doi = {10.1007/s11430-014-4978-1},
adsurl = {https://ui.adsabs.harvard.edu/abs/2015ScChD..58..371S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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