the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: methods for assessment of models 20 Jan 2021
Submitted as: methods for assessment of models | 20 Jan 2021
TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking and Analysis in Large Datasets
- 1Department of Land, Air and Water Resources, University of California, Davis, Davis, California
- 2Pennsylvania State University
- 3School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, New York
- 1Department of Land, Air and Water Resources, University of California, Davis, Davis, California
- 2Pennsylvania State University
- 3School of Marine and Atmospheric Sciences, State University of New York at Stony Brook, Stony Brook, New York
Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth-system datasets on either structured and unstructured (native) grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal 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, and gives several examples of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes.
Paul A. Ullrich et al.
Status: open (extended)
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RC1: 'Comment on gmd-2020-303', Hristo Chipilski, 07 Feb 2021
reply
TempestExtremes (TE) is a framework for the identification and tracking of features in Earth system datasets. The underlying paradigm behind TE relies on the construction of abstract functions (kernels) that can be called directly from the command line and controlled via a highly configurable set of user parameters. In this work, the authors extend the original version of TE by carefully documenting all newly added kernels. Using several examples based on societally important meteorological features, they also demonstrate how one can configure TE for specific Earth system applications by sequentially combining relevant algorithm kernels. The robustness of the enhanced TE package is evident in its successful application to different geophysical features and the agreement of the obtained results with past studies. Because the upgraded version of TE generalizes previous tracking methods, the presented work constitutes an important contribution to the Earth system community as a whole. In view of this scientific merit and the high clarity of presentation, I strongly recommend the publication of the manuscript in GMD after the authors address my fairly minor comments in the attached PDF document.
Paul A. Ullrich et al.
Model code and software
TempestExtremes v2.1 Archived Code Paul Ullrich, Colin Zarzycki, Elizabeth McClenny, Marielle Pinheiro, Alyssa Stansfield, and Kevin Reed https://doi.org/10.5281/zenodo.4385656
Paul A. Ullrich et al.
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