Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4717-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-4717-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Nov 2019
Research article |
| 19 Nov 2019
| 19 Nov 2019
Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
Joel Fiddes
CORRESPONDING AUTHOR
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Kristoffer Aalstad
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
Sebastian Westermann
Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway
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24 citations as recorded by crossref.
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- IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021) F. Avanzi et al. 10.5194/essd-15-639-2023
- Multi-decadal observations in the Alps reveal less and wetter snow, with increasing variability C. Marty et al. 10.3389/feart.2023.1165861
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
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- The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. 10.5194/essd-14-1707-2022
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- Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019) M. Baba et al. 10.3390/w13070890
- Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay M. Kronenberg et al. 10.5194/tc-16-5001-2022
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- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Using Decision Making under Deep Uncertainty (DMDU) approaches to support climate change adaptation of Swiss Ski Resorts S. Ashraf Vaghefi et al. 10.1016/j.envsci.2021.09.005
- TopoPyScale: A Python Package for Hillslope Climate Downscaling S. Filhol et al. 10.21105/joss.05059
- Pattern-based downscaling of snowpack variability in the western United States N. Gauthier et al. 10.1007/s00382-021-06094-z
- The changing thermal state of permafrost S. Smith et al. 10.1038/s43017-021-00240-1
24 citations as recorded by crossref.
- A Downscaling Intercomparison Study: The Representation of Slope- and Ridge-Scale Processes in Models of Different Complexity B. Kruyt et al. 10.3389/feart.2022.789332
- Mapping of snow water equivalent by a deep-learning model assimilating snow observations G. Cui et al. 10.1016/j.jhydrol.2022.128835
- Observed and predicted trends in Icelandic snow conditions for the period 1930–2100 D. Eythorsson et al. 10.5194/tc-17-51-2023
- CrocO_v1.0: a particle filter to assimilate snowpack observations in a spatialised framework B. Cluzet et al. 10.5194/gmd-14-1595-2021
- TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1 J. Fiddes et al. 10.5194/gmd-15-1753-2022
- A Framework for Untangling Transient Groundwater Mixing and Travel Times A. Popp et al. 10.1029/2020WR028362
- The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere S. Westermann et al. 10.5194/gmd-16-2607-2023
- The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model M. Langer et al. 10.5194/tc-18-363-2024
- IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021) F. Avanzi et al. 10.5194/essd-15-639-2023
- Multi-decadal observations in the Alps reveal less and wetter snow, with increasing variability C. Marty et al. 10.3389/feart.2023.1165861
- Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation E. Alonso-González et al. 10.5194/hess-27-4637-2023
- Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area E. Alonso-González et al. 10.5194/hess-25-4455-2021
- The S2M meteorological and snow cover reanalysis over the French mountainous areas: description and evaluation (1958–2021) M. Vernay et al. 10.5194/essd-14-1707-2022
- Simulating the effect of subsurface drainage on the thermal regime and ground ice in blocky terrain in Norway C. Renette et al. 10.5194/esurf-11-33-2023
- The state and future of the cryosphere in Central Asia M. Barandun et al. 10.1016/j.wasec.2020.100072
- Inferring surface energy fluxes using drone data assimilation in large eddy simulations N. Pirk et al. 10.5194/amt-15-7293-2022
- Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019) M. Baba et al. 10.3390/w13070890
- Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay M. Kronenberg et al. 10.5194/tc-16-5001-2022
- The Multiple Snow Data Assimilation System (MuSA v1.0) E. Alonso-González et al. 10.5194/gmd-15-9127-2022
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Using Decision Making under Deep Uncertainty (DMDU) approaches to support climate change adaptation of Swiss Ski Resorts S. Ashraf Vaghefi et al. 10.1016/j.envsci.2021.09.005
- TopoPyScale: A Python Package for Hillslope Climate Downscaling S. Filhol et al. 10.21105/joss.05059
- Pattern-based downscaling of snowpack variability in the western United States N. Gauthier et al. 10.1007/s00382-021-06094-z
- The changing thermal state of permafrost S. Smith et al. 10.1038/s43017-021-00240-1
Latest update: 19 Apr 2024
Short summary
In this paper we address one of the big challenges in snow hydrology, namely the accurate simulation of the seasonal snowpack in ungauged regions. We do this by assimilating satellite observations of snow cover into a modelling framework. Importantly (and a novelty of the paper), we include a clustering approach that permits highly efficient ensemble simulations. Efficiency gains and dependency on purely global datasets, means that this method can be applied over large areas anywhere on Earth.
In this paper we address one of the big challenges in snow hydrology, namely the accurate...
