Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 1
Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-301-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-301-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 1
Methods for assessment of models
 | 
15 Jan 2024
Methods for assessment of models |  | 15 Jan 2024

Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets

Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu

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Short summary
This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios. 
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