Chad Shouquan Cheng

An Automated Synoptic Typing Procedure to Predict Freezing Rain: An Application to Ottawa, Ontario, Canada

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 (developmen...

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

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...

A Synoptic Weather-Typing Approach to Project Future Daily Rainfall and Extremes at Local Scale in Ontario, Canada

AbstractThis 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 ...

Possible Impacts of Climate Change on Wind Gusts under Downscaled Future Climate Conditions: Updated for Canada

AbstractThe 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%...

Possible Impacts of Climate Change on Wind Gusts under Downscaled Future Climate Conditions over Ontario, Canada

AbstractHourly/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 downscale...