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Chad Shouquan Cheng, Guilong Li, Qian Li, and Heather Auld

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%).

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Chad Shouquan Cheng, Guilong Li, Qian Li, and Heather Auld

Abstract

This paper attempts to project possible changes in the frequency of daily rainfall events late in this century for four selected river basins (i.e., Grand, Humber, Rideau, and Upper Thames) in Ontario, Canada. To achieve this goal, automated synoptic weather typing as well as cumulative logit and nonlinear regression methods was employed to develop within-weather-type daily rainfall simulation models. In addition, regression-based downscaling was applied to downscale four general circulation model (GCM) simulations to three meteorological stations (i.e., London, Ottawa, and Toronto) within the river basins for all meteorological variables (except rainfall) used in the study. Using downscaled GCM hourly climate data, discriminant function analysis was employed to allocate each future day for two windows of time (2046–65, 2081–2100) into one of the weather types. Future daily rainfall and its extremes were projected by applying within-weather-type rainfall simulation models together with downscaled future GCM climate data. A verification process of model results has been built into the whole exercise (i.e., statistical downscaling, synoptic weather typing, and daily rainfall simulation modeling) to ascertain whether the methods are stable for projection of changes in frequency of future daily rainfall events.

Two independent approaches were used to project changes in frequency of daily rainfall events: method I—comparing future and historical frequencies of rainfall-related weather types, and method II—applying daily rainfall simulation models with downscaled future climate information. The increases of future daily rainfall event frequencies and seasonal rainfall totals (April–November) projected by method II are usually greater than those derived by method I. The increase in frequency of future daily heavy rainfall events greater than or equal to 25 mm, derived from both methods, is likely to be greater than that of future daily rainfall events greater than or equal to 0.2 mm: 35%–50% versus 10%–25% over the period 2081–2100 derived from method II. In addition, the return values of annual maximum 3-day accumulated rainfall totals are projected to increase by 20%–50%, 30%–55%, and 25%–60% for the periods 2001–50, 2026–75, and 2051–2100, respectively. Inter-GCM and interscenario uncertainties of future rainfall projections were quantitatively assessed. The intermodel uncertainties are similar to the interscenario uncertainties, for both method I and method II. However, the uncertainties are generally much smaller than the projection of percentage increases in the frequency of future seasonal rain days and future seasonal rainfall totals. The overall mean projected percentage increases are about 2.6 times greater than overall mean intermodel and interscenario uncertainties from method I; the corresponding projected increases from method II are 2.2–3.7 times greater than overall mean uncertainties.

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Chad Shouquan Cheng, Edwina Lopes, Chao Fu, and Zhiyong Huang

Abstract

The methods used in earlier research focusing on the province of Ontario, Canada, were adapted for the current paper to expand the study area over the entire nation of Canada where various industries (e.g., transportation, agriculture, energy, and commerce) and infrastructure are at risk of being impacted by extreme wind gust events. The possible impacts of climate change on future wind gust events across Canada were assessed using a three-step process: 1) development and validation of hourly and daily wind gust simulation models, 2) statistical downscaling to derive future station-scale hourly wind speed data, and 3) projection of changes in the frequency of future wind gust events. The wind gust simulation models could capture the historically observed daily and hourly wind gust events. For example, the percentage of excellent and good validations for hourly wind gust events ≥90 km h−1 ranges from 62% to 85% across Canada; the corresponding percentage for wind gust events ≥40 km h−1 is about 90%. For future projection, the modeled results indicated that the frequencies of the wind gust events could increase late this century over Canada using the ensemble of the downscaled eight-GCM simulations [Special Report on Emissions Scenarios (SRES) A2 and B1]. For example, the percentage increases in future daily wind gust events ≥70 km h−1 from the current condition could be 10%–20% in most of the regions across Canada; the corresponding increases in future hourly wind gust events ≥70 km h−1 are projected to be 20%–30%. In addition, the inter-GCM and interscenario uncertainties of future wind gust projections were quantitatively assessed.

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Chad Shouquan Cheng, Guilong Li, Qian Li, Heather Auld, and Chao Fu

Abstract

Hourly/daily wind gust simulation models and regression-based downscaling methods were developed to assess possible impacts of climate change on future hourly/daily wind gust events over the province of Ontario, Canada. Since the climate/weather validation process is critical, a formal model result verification process has been built into the analysis to ascertain whether the methods are suitable for future projections. The percentage of excellent and good simulations among all studied seven wind gust categories ranges from 94% to 100% and from 69% to 95%, respectively, for hourly and daily wind gusts, for both model development and validation.

The modeled results indicate that frequencies of future hourly/daily wind gust events are projected to increase late this century over the study area under a changing climate. For example, across the study area, the annual mean frequency of future hourly wind gust events ≥28, ≥40, and ≥70 km h−1 for the period 2081–2100 derived from the ensemble of downscaled eight-GCM A2 simulations is projected to be about 10%–15%, 10%–20%, and 20%–40% greater than the observed average during the period 1994–2007, respectively. The corresponding percentage increase for future daily wind gust events is projected to be <10%, ~10%, and 15%–25%. Inter-GCM-model and interscenario uncertainties of future wind gust projections were quantitatively assessed. On average, projected percentage increases in frequencies of future hourly/daily wind gust events ≥28 and ≥40 km h−1 are about 90%–100% and 60%–80% greater than inter-GCM-model–interscenario uncertainties, respectively. For wind gust events ≥70 km h−1, the corresponding projected percentage increases are about 25%–35% greater than the interscenario uncertainties and are generally similar to inter-GCM-model uncertainties.

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Chad Shouquan Cheng, Heather Auld, Guilong Li, Joan Klaassen, Bryan Tugwood, and Qian Li

Abstract

Freezing rain is a major weather hazard that can compromise human safety, significantly disrupt transportation, and damage and disrupt built infrastructure such as telecommunication towers and electrical transmission and distribution lines. In this study, an automated synoptic typing and logistic regression analysis were applied together to predict freezing rain events. The synoptic typing was developed using principal components analysis, an average linkage clustering procedure, and discriminant function analysis to classify the weather types most likely to be associated with freezing rain events for the city of Ottawa, Ontario, Canada. Meteorological data used in the analysis included hourly surface observations from the Ottawa International Airport and six atmospheric levels of 6-hourly NCEP–NCAR upper-air reanalysis weather variables for the winter months (Nov– Apr) of 1958/59–2000/01. The data were divided into two parts: a developmental dataset (1958/59–1990/91) for construction (development) of the model and an independent or validation dataset (1991/90–2000/01) for validation of the model. The procedure was able to successfully identify weather types that were most highly correlated with freezing rain events at Ottawa.

Stepwise logistic regression was performed on all days within the freezing rain–related weather types to analytically determine the meteorological variables that can be used as forecast predictors for the likelihood of freezing rain occurrence at Ottawa. The results show that the model is best able to identify freezing rain events lasting several hours during a day. For example, in the validation dataset, for likelihood values ≥0.6, the procedure was able to identify 74% of all freezing rain events lasting at least 6 h during a day. Similarly, the procedure was able to identify 91% of all freezing rain events occurring at least 8 h during a day. This study has further potential to be adapted to an operational forecast mode to assist in the prediction of major ice storms.

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