Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 15
Articles | Volume 16, issue 15
https://doi.org/10.5194/gmd-16-4521-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-4521-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 15
Development and technical paper
 | 
10 Aug 2023
Development and technical paper |  | 10 Aug 2023

Automatic snow type classification of snow micropenetrometer profiles with machine learning algorithms

Julia Kaltenborn, Amy R. Macfarlane, Viviane Clay, and Martin Schneebeli

Viewed

Total article views: 1,255 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
929 275 51 1,255 31 32
  • HTML: 929
  • PDF: 275
  • XML: 51
  • Total: 1,255
  • BibTeX: 31
  • EndNote: 32
Views and downloads (calculated since 06 Dec 2022)
Cumulative views and downloads (calculated since 06 Dec 2022)

Viewed (geographical distribution)

Total article views: 1,255 (including HTML, PDF, and XML) Thereof 1,231 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 May 2024
Download
Short summary
Snow layer segmentation and snow grain classification are essential diagnostic tasks for cryospheric applications. A SnowMicroPen (SMP) can be used to that end; however, the manual classification of its profiles becomes infeasible for large datasets. Here, we evaluate how well machine learning models automate this task. Of the 14 models trained on the MOSAiC SMP dataset, the long short-term memory model performed the best. The findings presented here facilitate and accelerate SMP data analysis.
Read more
Special issue
Benchmark datasets and machine learning algorithms for Earth...