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.
Methods for assessment of models
| 
13 Aug 2021
Methods for assessment of models |
| 13 Aug 2021
| 13 Aug 2021
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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- An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SM) W. Liu et al. 10.1029/2021MS002805
- Understanding Precipitation Bias Sensitivities in E3SM‐Multi‐Scale Modeling Framework From a Dilution Framework N. Liu et al. 10.1029/2022MS003460
- Global tropical cyclone precipitation scaling with sea surface temperature A. Stansfield & K. Reed 10.1038/s41612-023-00391-6
- Increased U.S. coastal hurricane risk under climate change K. Balaguru et al. 10.1126/sciadv.adf0259
- Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps M. Gore et al. 10.1029/2022EF003211
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Latest update: 13 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...
