Reliability framework for characterizing heat wave and cold spell events

Extreme weather events such as heat waves and cold spells affect people’s lives. This study develops a probabilistic framework to evaluate heat waves and cold spells. As case studies, average daily temperatures of meteorological stations of the two cities (Tehran and Vancouver) from 1995 to 2016 are used to identify four main indicators including intensity, average intensity, duration, and the rate of the occurrence. In addition, probabilistic spatial analysis of the events is obtained through MODIS’s land surface temperature product. To include possible uncertainties, the predictive probability distributions of the intensity and duration are derived using a Bayesian scheme and Monte Carlo Markov Chain method. The probability distributions of the indicators show that the most extreme temperature (lowest temperature) occurs during the cold spell. Reliability evaluations indicate that both cities are more likely to be affected by the cold spell than the heat wave. Results of this study can be used as a benchmark for heat wave/cold spell characterization. The developed approach can be applied to characterize other extreme weather events in any location.