Preprints
https://doi.org/10.5194/egusphere-2023-592
https://doi.org/10.5194/egusphere-2023-592
09 May 2023
 | 09 May 2023
Status: this preprint is open for discussion.

Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets

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

Abstract. This study describes a generalized framework, Scalable Feature Extraction and Tracking (SCAFET) to extract and track features from large climate datasets. SCAFET utilizes novel shape-based metrics that can efficiently identify and compare features from different mean states, datasets, and between distinct regions. Features of interest are extracted by segmenting the data based on a scale-independent bounded variable called shape index (SI). SI gives a quantitative measurement of the local geometric shape of the field with respect to its surroundings. To demonstrate the capabilities of the method, we illustrate the detection of atmospheric rivers, tropical and extratropical cyclones, sea surface temperature fronts, and jet streams. Cyclones and atmospheric rivers are extracted from the ERA5 reanalysis dataset to show how the algorithm extracts both locations and areas from climate datasets. The extraction of sea surface temperature fronts exemplifies how SCAFET effectively handles curvilinear grids. Lastly, jet streams are extracted to demonstrate how the algorithm can also detect 3D features. SCAFET can be implemented to extract and track most weather and climate features.

Arjun Babu Nellikkattil et al.

Status: open (until 04 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Arjun Babu Nellikkattil et al.

Data sets

Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets Arjun Babu Nellikkattil https://doi.org/10.5281/zenodo.7767301

Model code and software

Scalable Feature Extraction and Tracking (SCAFET): A general framework for feature extraction from large climate datasets Arjun Babu Nellikkattil https://doi.org/10.5281/zenodo.7767301

Arjun Babu Nellikkattil et al.

Viewed

Total article views: 163 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
111 49 3 163 14 2 1
  • HTML: 111
  • PDF: 49
  • XML: 3
  • Total: 163
  • Supplement: 14
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 09 May 2023)
Cumulative views and downloads (calculated since 09 May 2023)

Viewed (geographical distribution)

Total article views: 167 (including HTML, PDF, and XML) Thereof 167 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Jun 2023
Download
Short summary
The exponential increases in the climate and weather data demand computationally efficient and mathematically sound feature extraction algorithms to identify phenomenons such as atmospheric rivers, cyclones, sea surface temperature fronts, jet streams, etc. In this study, we present an innovative generalized framework for extracting two and three-dimensional features from gridded datasets using the local geometric shape of the input fields.