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

Ullrich, Paul A.; Zarzycki, Colin M.; McClenny, Elizabeth E.; Pinheiro, Marielle C.; Stansfield, Alyssa M.; Reed, Kevin A.

doi:10.5194/gmd-14-5023-2021

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

Journal Article · 13 August 2021 · Geoscientific Model Development (Online)

DOI: https://doi.org/10.5194/gmd-14-5023-2021 · OSTI ID:1852348

^[1]; Zarzycki, Colin M. ^[2]; McClenny, Elizabeth E. ^[3]; Pinheiro, Marielle C. ^[3]; Stansfield, Alyssa M. ^[4];

^[4]

Univ. of California, Davis, CA (United States). Dept. of Land, Air and Water Resources; Univ. of California, Davis, CA (United States)
Pennsylvania State Univ., University Park, PA (United States). Dept. of Meteorology and Atmospheric Science
Univ. of California, Davis, CA (United States). Dept. of Land, Air and Water Resources
State Univ. of New York (SUNY), Stony Brook, NY (United States). School of Marine and Atmospheric Sciences

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.

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Research Organization:: Univ. of California, Davis, CA (United States)

Sponsoring Organization:: National Aeronautics and Space Administration (NASA); National Oceanic and Atmospheric Administration (NOAA); USDOE Office of Science (SC)

Grant/Contract Number:: SC0016605

OSTI ID:: 1852348

Journal Information:: Geoscientific Model Development (Online), Journal Name: Geoscientific Model Development (Online) Journal Issue: 8 Vol. 14; ISSN 1991-9603

Publisher:: Copernicus Publications, EGUCopyright Statement

Country of Publication:: United States

Language:: English

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