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TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
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TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
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TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

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TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
Journal Article

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

2021
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Overview
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.