Development of a daily gridded snow water equivalent analysis

Mahmood, Md. Makamum (2013) Development of a daily gridded snow water equivalent analysis. Masters thesis, Memorial University of Newfoundland.

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Abstract

Currently, snow analysis for weather prediction in Canada is conducted using snow depth measurements alone. The current effort is intended to revisit this analysis using both snow depth and density measurements from snow course sites previously unused during weather prediction analysis. The purpose of this reanalysis is to produce a gridded daily Snow Water Equivalent (SWE) hindcast within the transect from the Great Lakes through Quebec and into Labrador. -- The final SWE prediction was produced by combining output from existing deterministic snow density models and developed statistical prediction models. Statistical Models were developed based on Universal Kriging (UK) interpolation technique on measured data and by considering the background fields. These fields include a number of physiographic variables and the Canadian Meteorological Centre (CMC) snow depth analysis product. -- Finally, the new product was evaluated from the calculated Root Mean Square Error (RMSE) of the predicted SWE at both validation and cross validation points, simulation vs. observation comparison, and also from a visual consistency check. The research produced a methodology for SWE prediction and daily gridded SWE product, which is a valuable attempt to improve hydrological prediction from snow melting. The average RMSE of SWE prediction was around 30-35 mm although, the validation of results were challenged by limited quantity of snow course data.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/10967
Item ID: 10967
Additional Information: Includes bibliographical references (leaves 104-115).
Department(s): Engineering and Applied Science, Faculty of
Date: 2013
Date Type: Submission
Library of Congress Subject Heading: Statistical weather forecasting; Snow--Measurement--Statistical methods; Precipitation forecasting--Statistical methods.

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