A Generalized Spatio-Temporal Threshold Selection Method for Identification of Extreme Event Patterns

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 extreme weather events may change as a result. Climate and statistical models are often independently used to model those phenomena. To better assess performance of the climate models, a variety of spatial forecast verification methods have been developed. However, spatial verification measures that are widely used in comparing mean states, in most cases, do not have an adequate theoretical justification to benchmark extreme weather events.  We propose a new generalized spatio-temporal threshold selection method as a part of integrated modeling framework for identification of extreme event episodes that couples existing pattern recognition indices with high (or low) threshold choices. This integrated approach has four main steps: 1). Construction of essential climate quantities; 2). Dimension reduction; 3). Spatial domain mapping; and 4). Thresholds clustering.  We apply this approach to the observed standardized precipitation rate anomalies over CONUS and the set of regional climate models to see if the models capture observed patterns of extreme episodes. The proposed method automates the threshold selection process and can be directly applicable in conjunction with modeling of extremes.  As an added bonus, it offers user a flexibility of selecting an extreme threshold that is linked to desired geometrical properties.  Another nice feature is to identify synoptic scale spatial patterns that can be directly traced to the individual extreme episodes.