Impact of quality control of satellite soil moisture data on their assimilation into land surface model

被引：40
作者：
Yin, Jifu [1 ,2 ]
Zhan, Xiwu [2 ]
Zheng, Youfei [3 ]
Liu, Jicheng [2 ,4 ]
Hain, Christopher R. [2 ,5 ]
Fang, Li [2 ,5 ]
机构：
[1] Nanjing Univ Informat Sci & Technol, China Meteorol Adm, Key Lab Aerosol Cloud Precipitat, Nanjing, Jiangsu, Peoples R China
[2] NOAA, NESDIS Ctr Satellite Applicat & Res, College Pk, MD USA
[3] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Atmospher Environm Monitoring & P, Nanjing, Jiangsu, Peoples R China
[4] George Mason Univ, Dept Geog & Geoinformat Sci, Fairfax, VA 22030 USA
[5] Univ Maryland, ESSIC CICS, College Pk, MD 20742 USA
来源：
GEOPHYSICAL RESEARCH LETTERS | 2014年 / 41卷 / 20期
关键词：
SMOPS soil moisture product; quality control; data assimilation; green vegetation fraction; PRECIPITATION; PRODUCTS; SKILL;
D O I：
10.1002/2014GL060659
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
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.
引用
收藏
页码：7159 / 7166
页数：8
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