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Impact of quality control of satellite soil moisture data on their assimilation into land surface model
Author(s) -
Yin Jifu,
Zhan Xiwu,
Zheng Youfei,
Liu Jicheng,
Hain Christopher R.,
Fang Li
Publication year - 2014
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2014gl060659
Subject(s) - data assimilation , environmental science , water content , numerical weather prediction , satellite , assimilation (phonology) , meteorology , moisture , vegetation (pathology) , geostationary operational environmental satellite , remote sensing , geography , geology , medicine , linguistics , philosophy , geotechnical engineering , pathology , aerospace engineering , engineering
A global Soil Moisture Operational Product System (SMOPS) has been developed to process satellite soil moisture observational data at the NOAA National Environmental Satellite, Data, and Information Service for improving numerical weather prediction (NWP) models at the NOAA National Weather Service (NWS). A few studies have shown the benefits of assimilating satellite soil moisture data in land surface models (LSMs), which are the components of most NWP models. In this study, synthetic experiments are conducted to determine how soil moisture data quality control may impact the benefit of their assimilation into LSMs. It is found that using green vegetation fraction to quality control the SMOPS soil moisture product may significantly increase the benefit of assimilating it into Noah LSM in terms of increasing the agreement of Noah LSM surface and root zone soil moisture simulations with the corresponding in situ measurements. The quality control procedures and parameters are suggested for the assimilation of SMOPS data into NWS NWP models.