Editorial Type:
Article
Article Type:
Research Article

A Synoptic Weather Typing Approach to Simulate Daily Rainfall and Extremes in Ontario, Canada: Potential for Climate Change Projections

Chad Shouquan Cheng Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch, Environment Canada, Toronto, Ontario, Canada

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Guilong Li Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch, Environment Canada, Toronto, Ontario, Canada

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Qian Li Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch, Environment Canada, Toronto, Ontario, Canada

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Heather Auld Adaptation and Impacts Research Section, Science and Technology Branch, Environment Canada, Toronto, Ontario, Canada

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Print Publication:
01 May 2010
DOI:
https://doi.org/10.1175/2010JAMC2016.1
Page(s):
845–866
Restricted access

Abstract

An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).

Corresponding author address: Dr. Chad Shouquan Cheng, Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch, Environment Canada, 4905 Dufferin Street, Toronto ON M3H 5T4, Canada. Email: shouquan.cheng@ec.gc.ca

Abstract

An automated synoptic weather typing and stepwise cumulative logit/nonlinear regression analyses were employed to simulate the occurrence and quantity of daily rainfall events. The synoptic weather typing was developed using principal component analysis, an average linkage clustering procedure, and discriminant function analysis to identify the weather types most likely to be associated with daily rainfall events for the four selected river basins in Ontario. Within-weather-type daily rainfall simulation models comprise a two-step process: (i) cumulative logit regression to predict the occurrence of daily rainfall events, and (ii) using probability of the logit regression, a nonlinear regression procedure to simulate daily rainfall quantities. The rainfall simulation models were validated using an independent dataset, and the results showed that the models were successful at replicating the occurrence and quantity of daily rainfall events. For example, the relative operating characteristics score is greater than 0.97 for rainfall events with daily rainfall ≥10 or ≥25 mm, for both model development and validation. For evaluation of daily rainfall quantity simulation models, four correctness classifications of excellent, good, fair, and poor were defined, based on the difference between daily rainfall observations and model simulations. Across four selected river basins, the percentage of excellent and good simulations for model development ranged from 62% to 84% (of 20 individuals, 16 cases ≥ 70%, 7 cases ≥ 80%); the corresponding percentage for model validation ranged from 50% to 76% (of 20 individuals, 15 cases ≥ 60%, 6 cases ≥ 70%).

Corresponding author address: Dr. Chad Shouquan Cheng, Atmospheric Science and Applications Unit, Meteorological Service of Canada Branch, Environment Canada, 4905 Dufferin Street, Toronto ON M3H 5T4, Canada. Email: shouquan.cheng@ec.gc.ca

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