DOI: 10.5194/tc-10-1021-2016
Scopus记录号: 2-s2.0-84969980363
论文题名: On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model
作者: Charrois L ; , Cosme E ; , Dumont M ; , Lafaysse M ; , Morin S ; , Libois Q ; , Picard G
刊名: Cryosphere
ISSN: 19940416
出版年: 2016
卷: 10, 期: 3 起始页码: 1021
结束页码: 1038
语种: 英语
英文关键词: 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
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). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75143
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Universite Grenoble Alpes, LGGE, CNRS, Grenoble, France; CEN, CNRS, CNRM, Grenoble, France
Recommended Citation:
Charrois L,, Cosme E,, Dumont M,et al. On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model[J]. Cryosphere,2016-01-01,10(3)