Abstract
Extreme weather events such as heat waves and cold spells affect people’s lives. This study uses a probabilistic framework to evaluate heat waves and cold spells in different regions (Tehran in Iran and Vancouver in Canada). Average daily temperatures of meteorological stations of the two cities from 1995 to 2016 are used to identify four main indicators including intensity, average intensity, duration, and the rate of the occurrence. In addition, average intensities of the events are obtained from the MODIS Land Surface Temperature (LST) in each pixel of the two cities. To include possible uncertainties, the predictive probability distributions of the intensity and duration are derived using a Bayesian scheme and Monte-Carlo Markov Chain (MCMC) method. The probability distributions of the indicators show that the most extreme temperature (lowest temperature) occurs during the cold spell. Results indicate that although Tehran is more probable to experience heat waves than Vancouver, both cities are more likely to be affected by the cold spell than the heat wave. The developed approach can be used to characterize other extreme weather events in any location.
Reliability Framework for Characterizing Heat Wave and Cold Spell Events
