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	Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction
	Yin, Jifu1,2,3; Zhan, Xiwu2; Zheng, Youfei4; Hain, Christopher R.2,3; Ek, Michael5; Wen, Jun1; Fang, Li2,3; Liu, Jicheng2,3
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2016-03-15
卷号	218页码:171-183
关键词	Green vegetation fraction Albedo Noah land surface model Soil moisture Soil temperature Near real time
ISSN号	0168-1923
DOI	10.1016/j.agrformet.2015.12,001
通讯作者	Yin, Jifu(jifu.yin@noaa.gov)
英文摘要	The current operational Noah land surface model (LSM) uses multi-year climatology of monthly green vegetation fraction (GVF) and the multi-year averages of land surface albedo data for several numerical weather predictions at National Centers for Environmental Predictions of National Oceanic and Atmospheric Administration. However, these static GVF and albedo data can only prescribe the multiannual means and lack the ability to capture near real time (NRT) vegetation status and land surface condition. In this study, the impact of NRT GVF and albedo on Noah LSM (version 3.2) performances are examined against in situ measurements of surface net long wave radiation and net short wave radiation from 7 U.S. Surface Radiation Budget Network stations, and soil temperature and soil moisture from 9 USDA Soil Climate Analysis Network sites. Large differences between the NRT GVF/surface albedo and their climatological averages are found over the global, which have significant influences on Noah LSM simulations. With respect to in situ measurements, the Noah LSM simulation improvements from using the weekly GVF data are 19.3% for surface soil moisture, 9.3% for surface soil temperature. The benefits from the weekly GVF and monthly albedo can reach to 2.7 W m(-2) for surface net long wave radiation and 2.6 W m(-2) for surface net short wave radiation. The results suggest to Noah model developers and users that the NRT GVF and albedo should be used for better model performance. (C) 2015 Elsevier B.V. All rights reserved.
收录类别	SCI
WOS关键词	DATA ASSIMILATION SYSTEM ; SOIL-MOISTURE OBSERVATIONS ; MESOSCALE ETA-MODEL ; IN-SITU ; COVER DATA ; DATA SET ; DROUGHT ; MODIS ; PARAMETERIZATION ; EVAPORATION
WOS研究方向	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
WOS类目	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000370905100016
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2557411
专题	寒区旱区环境与工程研究所
通讯作者	Yin, Jifu
作者单位	1.Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Key Lab Land Surface Proc & Climate Change Cold &, Lanzhou 730000, Gansu, Peoples R China 2.NOAA, NESDIS Ctr Satellite Applicat & Res, College Pk, MD 20740 USA 3.Univ Maryland, ESSIC, CICS, College Pk, MD 20740 USA 4.Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Atmospher Environm Monitoring & P, Nanjing 210044, Jiangsu, Peoples R China 5.NOAA, NCEP Environm Modeling Ctr, College Pk, MD 20740 USA
推荐引用方式 GB/T 7714	Yin, Jifu,Zhan, Xiwu,Zheng, Youfei,et al. Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction[J]. AGRICULTURAL AND FOREST METEOROLOGY,2016,218:171-183.
APA	Yin, Jifu.,Zhan, Xiwu.,Zheng, Youfei.,Hain, Christopher R..,Ek, Michael.,...&Liu, Jicheng.(2016).Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction.AGRICULTURAL AND FOREST METEOROLOGY,218,171-183.
MLA	Yin, Jifu,et al."Improving Noah land surface model performance using near real time surface albedo and green vegetation fraction".AGRICULTURAL AND FOREST METEOROLOGY 218(2016):171-183.

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