DOI: 10.5194/hess-23-4717-2019
论文题名: Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
作者: Fiddes J. ; Aalstad K. ; Westermann S.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2019
卷: 23, 期: 11 起始页码: 4717
结束页码: 4736
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
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. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162853
Appears in Collections: 气候变化与战略
There are no files associated with this item.
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
Recommended Citation:
Fiddes J.,Aalstad K.,Westermann S.. Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering[J]. Hydrology and Earth System Sciences,2019-01-01,23(11)