气候变化领域集成服务门户(CCPortal): Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering

CCPortal

DOI	10.5194/hess-23-4717-2019
	Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
	Fiddes J.; Aalstad K.; Westermann S.
发表日期	2019
ISSN	1027-5606
起始页码	4717
结束页码	4736
卷号	23 期号:11
英文摘要	Spatial variability in high-relief landscapes is immense, and grid-based models cannot be run at spatial resolutions to explicitly represent important physical processes. This hampers the assessment of the current and future evolution of important issues such as water availability or mass movement hazards. Here, we present a new processing chain that couples an efficient sub-grid method with a downscaling tool and a data assimilation method with the purpose of improving numerical simulation of surface processes at multiple spatial and temporal scales in ungauged basins. The novelty of the approach is that while we add 1-2 orders of magnitude of computational cost due to ensemble simulations, we save 4-5 orders of magnitude over explicitly simulating a high-resolution grid. This approach makes data assimilation at large spatio-temporal scales feasible. In addition, this approach utilizes only freely available global datasets and is therefore able to run globally. We demonstrate marked improvements in estimating snow height and snow water equivalent at various scales using this approach that assimilates retrievals from a MODIS snow cover product. We propose that this as a suitable method for a wide variety of operational and research applications where surface models need to be run at large scales with sparse to non-existent ground observations and with the flexibility to assimilate diverse variables retrieved by Earth observation missions. © 2019 Copernicus GmbH. All rights reserved.
语种	英语
scopus关键词	Numerical methods; Data assimilation methods; Ground observations; High-resolution grids; Research applications; Snow water equivalent; Snow-cover products; Spatial and temporal scale; Spatio-temporal scale; Snow; cluster analysis; data assimilation; MODIS; mountain region; satellite mission; simulation; snow cover; snow water equivalent; spatial resolution; spatiotemporal analysis; water availability
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/159563
作者单位	Fiddes, J., WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland, Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, Oslo, 0316, Norway; Aalstad, K., Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, Oslo, 0316, Norway; Westermann, S., Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, Oslo, 0316, Norway
推荐引用方式 GB/T 7714	Fiddes J.,Aalstad K.,Westermann S.. Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering[J],2019,23(11).
APA	Fiddes J.,Aalstad K.,&Westermann S..(2019).Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering.Hydrology and Earth System Sciences,23(11).
MLA	Fiddes J.,et al."Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering".Hydrology and Earth System Sciences 23.11(2019).