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Keywords:

  • statistical downscaling;
  • stochastic disaggregation;
  • meteorological ensemble prediction system;
  • rainfall forecasts

Abstract

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast. However, for hydrological forecasting, their low resolution currently limits their use to large watersheds. To bridge this gap, various implementations of a spatial statistical downscaling method were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 × 70-km resolution down to 6 × 4-km while increasing each pixel's rainfall variance and preserving its original mean. This was applied for nine consecutive days of summer 2009 with strong rain events over Quebec City, Canada. For comparison purposes, simpler methods were also implemented such as the bilinear interpolation, which disaggregates global forecasts without modifying their variance.

The meteorological products were evaluated, using different scores and diagrams, against observed values taken from Quebec City rain gauge network. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using the variance-enhancing method were of similar quality than bilinear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared with the bilinear interpolation, which is a decisive advantage. Therefore, there is an interest in implementing variance-enhancing methods to disaggregate global ensemble rainfall forecasts. Copyright © 2012 Her Majesty the Queen in right of Canada. Published by John Wiley & Sons, Ltd.