气候变化领域集成服务门户(CCPortal): On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model

CCPortal

DOI	10.5194/tc-10-1021-2016
	On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model
	Charrois L.; Cosme E.; Dumont M.; Lafaysse M.; Morin S.; Libois Q.; Picard G.
发表日期	2016
ISSN	19940416
卷号	10 期号:3
英文摘要	This paper examines the ability of optical reflectance data assimilation to improve snow depth and snow water equivalent simulations from a chain of models with the SAFRAN meteorological model driving the detailed multilayer snowpack model Crocus now including a two-stream radiative transfer model for snow, TARTES. The direct use of reflectance data, allowed by TARTES, instead of higher level snow products, mitigates uncertainties due to commonly used retrieval algorithms. Data assimilation is performed with an ensemble-based method, the Sequential Importance Resampling Particle filter, to represent simulation uncertainties. In snowpack modeling, uncertainties of simulations are primarily assigned to meteorological forcings. Here, a method of stochastic perturbation based on an autoregressive model is implemented to explicitly simulate the consequences of these uncertainties on the snowpack estimates. Through twin experiments, the assimilation of synthetic spectral reflectances matching the MODerate resolution Imaging Spectroradiometer (MODIS) spectral bands is examined over five seasons at the Col du Lautaret, located in the French Alps. Overall, the assimilation of MODIS-like data reduces by 45% the root mean square errors (RMSE) on snow depth and snow water equivalent. At this study site, the lack of MODIS data on cloudy days does not affect the assimilation performance significantly. The combined assimilation of MODIS-like reflectances and a few snow depth measurements throughout the 2010/2011 season further reduces RMSEs by roughly 70 %. This work suggests that the assimilation of optical reflectances has the potential to become an essential component of spatialized snowpack simulation and forecast systems. The assimilation of real MODIS data will be investigated in future works. © 2016 Author(s).
学科领域	algorithm; data assimilation; depth determination; forecasting method; meteorology; MODIS; perturbation; radiative transfer; reflectance; snow water equivalent; snowpack; spectral reflectance; stochasticity; Alps; France; Hautes Alpes; Provence-Alpes-Cote d'Azur; Crocus
语种	英语
scopus关键词	algorithm; data assimilation; depth determination; forecasting method; meteorology; MODIS; perturbation; radiative transfer; reflectance; snow water equivalent; snowpack; spectral reflectance; stochasticity; Alps; France; Hautes Alpes; Provence-Alpes-Cote d'Azur; Crocus
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/119648
作者单位	Universite Grenoble Alpes, LGGE, CNRS, Grenoble, France; CEN, CNRS, CNRM, Grenoble, France
推荐引用方式 GB/T 7714	Charrois L.,Cosme E.,Dumont M.,et al. On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model[J],2016,10(3).
APA	Charrois L..,Cosme E..,Dumont M..,Lafaysse M..,Morin S..,...&Picard G..(2016).On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model.Cryosphere,10(3).
MLA	Charrois L.,et al."On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model".Cryosphere 10.3(2016).