An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-01

AUTHORS

J. Mailhot, J. A. Milbrandt, A. Giguère, R. McTaggart-Cowan, A. Erfani, B. Denis, A. Glazer, M. Vallée

ABSTRACT

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM–LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow. More... »

PAGES

209-229

Journal

TITLE

Pure and Applied Geophysics

ISSUE

1-2

VOLUME

171

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00024-012-0520-6

DOI

http://dx.doi.org/10.1007/s00024-012-0520-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023850888


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