气候变化领域集成服务门户(CCPortal): Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements

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DOI	10.5194/tc-9-13-2015
	Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements
	Hedrick A.; Marshall H.-P.; Winstral A.; Elder K.; Yueh S.; Cline D.
发表日期	2015
ISSN	19940416
卷号	9 期号:1
英文摘要	Repeated light detection and ranging (lidar) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 lidar-derived data set of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the conterminous United States. Independent validation data are scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation data set with substantial geographic coverage. Within 12 distinctive 500 × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 lidar acquisitions. This supplied a data set for constraining the uncertainty of upscaled lidar estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled lidar snow depths were then compared to the SNODAS estimates over the entire study area for the dates of the lidar flights. The remotely sensed snow depths provided a more spatially continuous comparison data set and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between lidar observations and SNODAS estimates were most drastic, providing insight into the causal influences of natural processes on model uncertainty. © 2015 Author(s).
学科领域	data assimilation; data set; lidar; remote sensing; snow cover; snowpack; United States
语种	英语
scopus关键词	data assimilation; data set; lidar; remote sensing; snow cover; snowpack; United States
来源期刊	Cryosphere
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/119975
作者单位	Boise State University, Center for Geophysical Investigation of Shallow Subsurface, Boise, ID 83725, United States; USDA, ARS Northwest Watershed Research Center, 800 Park Blvd., Boise, ID 83712, United States; USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO 80526, United States; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, United States; NOAA-NWS, Hydrology Laboratory, Office of Hydrologic Development, Silver Spring, MD 20910, United States
推荐引用方式 GB/T 7714	Hedrick A.,Marshall H.-P.,Winstral A.,et al. Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements[J],2015,9(1).
APA	Hedrick A.,Marshall H.-P.,Winstral A.,Elder K.,Yueh S.,&Cline D..(2015).Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements.Cryosphere,9(1).
MLA	Hedrick A.,et al."Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements".Cryosphere 9.1(2015).