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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- Evaluating the Simulation of CONUS Precipitation by Storm Type in E3SM K. Reed et al. 10.1029/2022GL102409
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- Intercomparison of four algorithms for detecting tropical cyclones using ERA5 S. Bourdin et al. 10.5194/gmd-15-6759-2022
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- Bias Analysis in the Simulation of the Western North Pacific Tropical Cyclone Characteristics by Two High-Resolution Global Atmospheric Models Q. Liu et al. 10.1007/s00376-022-2159-2
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- Convection in future winter storms over Northern Europe S. Berthou et al. 10.1088/1748-9326/aca03a
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- Tropical cyclone strength, precipitation, and environment in variable resolution CAM-MPAS simulations over Western North Pacific Y. Liang et al. 10.1007/s00382-023-06677-y
- Role of Tropical Cyclones in Determining ENSO Characteristics H. Li et al. 10.1029/2022GL101814
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- Subseasonal Tropical Cyclone Prediction and Modulations by MJO and ENSO in CESM2 H. Li et al. 10.1029/2022JD036986
- Increasing Hurricane Intensification Rate Near the US Atlantic Coast K. Balaguru et al. 10.1029/2022GL099793
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- The Multi‐Scale Interactions of Atmospheric Phenomenon in Mean and Extreme Precipitation A. Prein et al. 10.1029/2023EF003534
- 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
- Impact of Grids and Dynamical Cores in CESM2.2 on the Surface Mass Balance of the Greenland Ice Sheet A. Herrington et al. 10.1029/2022MS003192
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Latest update: 02 Dec 2023
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...
