Capturing the time-dependence in the precipitation process for weather risk assessment

Abstract

This work investigates the seasonal, long-term and time-dependence properties of the precipitation process using daily precipitation amount records in Calgary and develops occurrence–amount models which capture the complex properties of the process. Data show that: (a) the probability of precipitation occurrence not only depends on the occurrence of the previous days, but also depends on the amount values. Moreover this dependence varies with season. (b) The expected amount and its volatility depend on the occurrence as well as the amount on the previous days—these dependencies reveal seasonal patterns. (c) There is a strong long-term dependence in Calgary’s data. The proposed models in this work satisfy these properties. A large set of covariates is built to capture the complexity of various properties of the precipitation process. A grouped sequential model selection approach is used to pick the appropriate covariates which works by assigning the predictors to various groups and sequentially exploring them. This framework is assessed by comparing the simulations from the models to the observed data. This confirms a satisfactory agreement.