Evaluation of Snowpack Simulations over the Canadian Rockies with an Experimental Hydrometeorological Modeling System

Marco L. Carrera Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada

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Stéphane Bélair Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada

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Vincent Fortin Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada

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Bernard Bilodeau Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada

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Dorothée Charpentier Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada

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Isabelle Doré Meteorological Research Division, Environment Canada, Dorval, Quebec, Canada

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Abstract

To improve the representation of the land surface in their operational numerical weather prediction (NWP) models, the Meteorological Research Division of Environment Canada (EC) is developing an external hydrometeorological modeling and data assimilation system. The objective of this study is to verify the improvement in simulating snow cover extent (SCE) and snow water equivalent (SWE) over the Canadian Rockies with this new modeling system. This study will be an important first step in determining the optimal configuration of the land surface model and atmospheric forcing for a future operational implementation. Simulated SCE is compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) analysis, while simulated SWE values are verified against a series of manual snow survey sites located within the Canadian Rockies. Results show that land surface model simulations of SCE and SWE were sensitive to precipitation forcing. Simulations at both low and high resolution forced with EC’s experimental precipitation analysis were found to underestimate SCE and SWE values. Mountain snowpack retreated too early during the spring melt period. Precipitation forcing derived from EC’s short-range NWP model resulted in improved values for both SCE and SWE, which also contributed to higher contributions to streamflow. Terrain adjusting the atmospheric forcing data was found to be important for properly modeling local extreme SWE values. A comparison with available precipitation observations over the Canadian Rockies region found EC’s experimental precipitation analysis to possess a negative precipitation bias that increases with increasing elevation.

Corresponding author address: Dr. Marco L. Carrera, Meteorological Research Division, Environment Canada, Canadian Meteorological Centre, 5th Floor, 2121 Trans-Canada Highway, Dorval QC H9P 1J3, Canada. Email: marco.carrera@ec.gc.ca

Abstract

To improve the representation of the land surface in their operational numerical weather prediction (NWP) models, the Meteorological Research Division of Environment Canada (EC) is developing an external hydrometeorological modeling and data assimilation system. The objective of this study is to verify the improvement in simulating snow cover extent (SCE) and snow water equivalent (SWE) over the Canadian Rockies with this new modeling system. This study will be an important first step in determining the optimal configuration of the land surface model and atmospheric forcing for a future operational implementation. Simulated SCE is compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) analysis, while simulated SWE values are verified against a series of manual snow survey sites located within the Canadian Rockies. Results show that land surface model simulations of SCE and SWE were sensitive to precipitation forcing. Simulations at both low and high resolution forced with EC’s experimental precipitation analysis were found to underestimate SCE and SWE values. Mountain snowpack retreated too early during the spring melt period. Precipitation forcing derived from EC’s short-range NWP model resulted in improved values for both SCE and SWE, which also contributed to higher contributions to streamflow. Terrain adjusting the atmospheric forcing data was found to be important for properly modeling local extreme SWE values. A comparison with available precipitation observations over the Canadian Rockies region found EC’s experimental precipitation analysis to possess a negative precipitation bias that increases with increasing elevation.

Corresponding author address: Dr. Marco L. Carrera, Meteorological Research Division, Environment Canada, Canadian Meteorological Centre, 5th Floor, 2121 Trans-Canada Highway, Dorval QC H9P 1J3, Canada. Email: marco.carrera@ec.gc.ca

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