Articles | Volume 19, issue 7
https://doi.org/10.5194/hess-19-3153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-3153-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework
M. S. Raleigh
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, Colorado, USA
J. D. Lundquist
Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
M. P. Clark
National Center for Atmospheric Research, Boulder, Colorado, USA
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116 citations as recorded by crossref.
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- Satellite-derived products of solar and longwave irradiances used for snowpack modelling in mountainous terrain L. Quéno et al. 10.5194/hess-24-2083-2020
- Toward Snow Cover Estimation in Mountainous Areas Using Modern Data Assimilation Methods: A Review C. Largeron et al. 10.3389/feart.2020.00325
- Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments E. Cho et al. 10.5194/tc-17-3915-2023
- Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent E. Cho et al. 10.5194/hess-26-5721-2022
- Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions C. Marin et al. 10.5194/tc-14-935-2020
- What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling T. Wagener & F. Pianosi 10.1016/j.earscirev.2019.04.006
- Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments S. Terzago et al. 10.5194/hess-24-4061-2020
- Snow Sublimation in Mountain Environments and Its Sensitivity to Forest Disturbance and Climate Warming G. Sexstone et al. 10.1002/2017WR021172
- Downscaling Snow Deposition Using Historic Snow Depth Patterns: Diagnosing Limitations From Snowfall Biases, Winter Snow Losses, and Interannual Snow Pattern Repeatability J. Pflug et al. 10.1029/2021WR029999
- WITHDRAWN: Incorporating IMERG satellite precipitation uncertainty into seasonal and peak streamflow predictions using the Hillslope Link hydrological model S. Hartke et al. 10.1016/j.jhydrol.2022.129012
- Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range B. Henn et al. 10.1016/j.jhydrol.2016.08.009
- A multiphysical ensemble system of numerical snow modelling M. Lafaysse et al. 10.5194/tc-11-1173-2017
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- Fractional snow-covered area: scale-independent peak of winter parameterization N. Helbig et al. 10.5194/tc-15-615-2021
- Examining the operational use of avalanche problems with decision trees and model-generated weather and snowpack variables S. Horton et al. 10.5194/nhess-20-3551-2020
- A large-scale validation of snowpack simulations in support of avalanche forecasting focusing on critical layers F. Herla et al. 10.5194/nhess-24-2727-2024
- Including Parameter Uncertainty in an Intercomparison of Physically-Based Snow Models D. Günther et al. 10.3389/feart.2020.542599
- A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland) R. Essery et al. 10.5194/gi-5-219-2016
- Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment J. Nousu et al. 10.5194/hess-28-4643-2024
- Does Data Availability Constrain Temperature-Index Snow Models? A Case Study in a Humid Boreal Forest A. Parajuli et al. 10.3390/w12082284
- Independent Evaluation of Frozen Precipitation from WRF and PRISM in the Olympic Mountains W. Currier et al. 10.1175/JHM-D-17-0026.1
- How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations?* M. Raleigh et al. 10.1175/JHM-D-14-0235.1
- On the assimilation of optical reflectances and snow depth observations into a detailed snowpack model L. Charrois et al. 10.5194/tc-10-1021-2016
- An assessment of differences in gridded precipitation datasets in complex terrain B. Henn et al. 10.1016/j.jhydrol.2017.03.008
- Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017) G. Sexstone et al. 10.1175/JHM-D-20-0077.1
- Multi-physics ensemble snow modelling in the western Himalaya D. Pritchard et al. 10.5194/tc-14-1225-2020
- 57 years (1960–2017) of snow and meteorological observations from a mid-altitude mountain site (Col de Porte, France, 1325 m of altitude) Y. Lejeune et al. 10.5194/essd-11-71-2019
- Thermal infrared shadow-hiding in GOES-R ABI imagery: snow and forest temperature observations from the SnowEx 2020 Grand Mesa field campaign S. Pestana et al. 10.5194/tc-18-2257-2024
- Windmapper: An Efficient Wind Downscaling Method for Hydrological Models C. Marsh et al. 10.1029/2022WR032683
- Interpreting Sentinel-1 SAR Backscatter Signals of Snowpack Surface Melt/Freeze, Warming, and Ripening, through Field Measurements and Physically-Based SnowModel J. Lund et al. 10.3390/rs14164002
- Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget K. Jennings et al. 10.5194/tc-12-1595-2018
- Spatial and temporal patterns of snowmelt refreezing in a Himalayan catchment S. Veldhuijsen et al. 10.1017/jog.2021.101
- Projected climate change impacts on skiing and snowmobiling: A case study of the United States C. Wobus et al. 10.1016/j.gloenvcha.2017.04.006
- Improving hydropower inflow forecasts by assimilating snow data J. Magnusson et al. 10.2166/nh.2020.025
- Simulation and Assimilation of Passive Microwave Data Using a Snowpack Model Coupled to a Calibrated Radiative Transfer Model Over Northeastern Canada F. Larue et al. 10.1029/2017WR022132
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Short summary
A sensitivity analysis is used to examine how error characteristics (type, distributions, and magnitudes) in meteorological forcing data impact outputs from a physics-based snow model in four climates. Bias and error magnitudes were key factors in model sensitivity and precipitation bias often dominated. However, the relative importance of forcings depended somewhat on the selected model output. Forcing uncertainty was comparable to model structural uncertainty as found in other studies.
A sensitivity analysis is used to examine how error characteristics (type, distributions, and...