What does an Empirical Spatial-Temporal Propinquity Model Reveal About the Trend in Frequency of Extreme Precipitation?

Extreme weather and climate events such as heavy precipitation, drought, heat waves and strong winds can cause extensive damage to the society in terms of human lives and financial losses. As climate changes, it is important to understand how spatial distribution of extreme weather events may change as a result.

Two approaches are employed to represent spatial dependence of extreme field. First, Conventional Location Specific Threshold (CLST) method is applied at individual locations with quantile-based thresholds. Second, rather than considering extreme values at individual locations and their temporal dependence, we consider an overall spatial field that is conditioned on being extreme by utilizing a new generalized Spatio-Temporal Threshold Clustering (STTC) method.

We apply these methods to an observed precipitation dataset over CONUS. The dataset is stratified annually and by seasons and model results are compared by mean error distance metric and by three geometric indices defined in AghaKouchak et. al (2010). Ultimately, we use supervised machine learning (random forest) classification model to rank importance of the geometric quantities resulted from CLST and STTC empirical models.

This work is a first step to utilize a novel approach in two model comparison cases to help to deepen our understanding on added value (i.e. connection between individual grid cells) that multivariate spatial model represents for spatial dependence analysis of extremes fields that are not analyzed in traditional univariate sense. We intend to expand this approach to compare climate and statistical model forecasts of extremes in our future research.