Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5023-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5023-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Colin M. Zarzycki
Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
Elizabeth E. McClenny
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Marielle C. Pinheiro
Department of Land, Air and Water Resources, University of California, Davis, CA, USA
Alyssa M. Stansfield
School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY, USA
Kevin A. Reed
School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, NY, USA
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Latest update: 08 Dec 2024
Short summary
TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific...