Abstract
Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.
Highlights
For many atmospheric and oceanic features, automated object identification and tracking in large datasets has enabled the targeted scientific exploration of feature-specific processes
The final threshold “zs,
Our goal is to reproduce this result in 20 years of ERA5 reanalysis using the Tempest Atmospheric rivers (ARs) detection algorithm (Shields et al, 2018; Rhoades et al, 2020b, a; McClenny et al, 2020)
Summary
For many atmospheric and oceanic features, automated object identification and tracking in large datasets has enabled the targeted scientific exploration of feature-specific processes. TempestExtremes (TE; Ullrich and Zarzycki, 2017) has been continuously augmented with new kernels – that is, basic data operators that can act as building-blocks for more complicated tracking algorithms – designed to streamline data analysis and generalize capabilities present in other trackers. These kernels provide more options and flexibility in exploring the space of trackers for each feature and enable a deeper understanding of how robust a given scientific conclusion is with respect to the choice of tracker.
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