Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets

Abstract

This study describes a generalized computational mathematical framework, Scalable Feature Extraction and Tracking (SCAFET), that extracts and tracks features from large climate data sets. SCAFET utilizes novel shape-based metrics that can identify and compare features from different mean states, data sets, and between distinct regions. Features of interest such as atmospheric rivers, tropical and extratropical cyclones, and jet streams are extracted by segmenting the data based on a scale-independent bounded variable called the shape index (SI). The SI gives a quantitative measurement of the local geometric shape of the field with respect to its surroundings. Compared to other widely used frameworks in feature detection, SCAFET does not use a posteriori assumptions about the climate model or mean state to extract features of interest and levelize the comparison between different models and scenarios. To demonstrate the capabilities of the method, we illustrate the detection of atmospheric rivers, tropical and extratropical cyclones, sea surface temperature fronts, and jet streams. Cyclones and atmospheric rivers are extracted to show how the algorithm identifies and tracks both the nodes and areas from climate data sets. The extraction of sea surface temperature fronts exemplifies how SCAFET effectively handles curvilinear grids. Last, jet streams are extracted to demonstrate how the algorithm can also detect three-dimensional features. As a generalized framework, SCAFET can be implemented to extract and track many weather and climate features across scales, grids, and dimensions.