气候变化领域集成服务门户(CCPortal): A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment

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DOI	10.5194/tc-12-2287-2018
	A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment
	Piazzi G.; Thirel G.; Campo L.; Gabellani S.
发表日期	2018
ISSN	19940416
卷号	12 期号:7
英文摘要	The accuracy of hydrological predictions in snow-dominated regions deeply depends on the quality of the snowpack simulations, with dynamics that strongly affect the local hydrological regime, especially during the melting period. With the aim of reducing the modelling uncertainty, data assimilation techniques are increasingly being implemented for operational purposes. This study aims to investigate the performance of a multivariate sequential importance resampling-particle filter scheme, designed to jointly assimilate several ground-based snow observations. The system, which relies on a multilayer energy-balance snow model, has been tested at three Alpine sites: Col de Porte (France), Torgnon (Italy), and Weissfluhjoch (Switzerland). The implementation of a multivariate data assimilation scheme faces several challenging issues, which are here addressed and extensively discussed: (1) the effectiveness of the perturbation of the meteorological forcing data in preventing the sample impoverishment; (2) the impact of the parameter perturbation on the filter updating of the snowpack state; the system sensitivity to (3) the frequency of the assimilated observations, and (4) the ensemble size. The perturbation of the meteorological forcing data generally turns out to be insufficient for preventing the sample impoverishment of the particle sample, which is highly limited when jointly perturbating key model parameters. However, the parameter perturbation sharpens the system sensitivity to the frequency of the assimilated observations, which can be successfully relaxed by introducing indirectly estimated information on snow-mass-related variables. The ensemble size is found not to greatly impact the filter performance in this point-scale application. © Author(s) 2018.
学科领域	algorithm; alpine environment; data assimilation; hydrological regime; modeling; multivariate analysis; numerical method; snow cover; snowpack; France; Graubunden; Italy; Switzerland; Weissfluhjoch
语种	英语
scopus关键词	algorithm; alpine environment; data assimilation; hydrological regime; modeling; multivariate analysis; numerical method; snow cover; snowpack; France; Graubunden; Italy; Switzerland; Weissfluhjoch
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/119107
作者单位	CIMA Research Foundation, Savona, 17100, Italy; Catchment Hydrology Research Group, HYCAR Research Unit, Irstea, Antony, 92160, France
推荐引用方式 GB/T 7714	Piazzi G.,Thirel G.,Campo L.,et al. A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment[J],2018,12(7).
APA	Piazzi G.,Thirel G.,Campo L.,&Gabellani S..(2018).A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment.Cryosphere,12(7).
MLA	Piazzi G.,et al."A particle filter scheme for multivariate data assimilation into a point-scale snowpack model in an Alpine environment".Cryosphere 12.7(2018).