A Generalized Spatiotemporal 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 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 extreme events. To better assess performance of the climate models, a variety of spatial forecast verification methods have been developed. However, in most cases the spatial verification measures that are widely used to compare mean states do not have sufficient theoretical justification to benchmark extreme weather events. As part of an integrated modeling framework, we propose a new generalized spatio-temporal threshold selection method for the identification of extreme event episodes, which 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). Threshold clustering. We apply this approach to observed standardized precipitation rate anomalies over CONUS and compare them to the set of regional climate models to determine whether 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. Additionally, it allows for identification of synoptic scale spatial patterns that can be directly traced to individual extreme episodes, and it offers users the flexibility to select an extreme threshold that is linked to desired geometrical properties.