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

Viewed

Total article views: 4,758 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,063 1,548 147 4,758 299 189 286
  • HTML: 3,063
  • PDF: 1,548
  • XML: 147
  • Total: 4,758
  • Supplement: 299
  • BibTeX: 189
  • EndNote: 286
Views and downloads (calculated since 09 May 2023)
Cumulative views and downloads (calculated since 09 May 2023)

Viewed (geographical distribution)

Total article views: 4,758 (including HTML, PDF, and XML) Thereof 4,676 with geography defined and 82 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 May 2026
Download
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. 
Read more
Share