Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 18
Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6541-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6541-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 18
Methods for assessment of models
 | 
29 Sep 2025
Methods for assessment of models |  | 29 Sep 2025

Implementation of implicit filters for spatial spectra extraction

Kacper Nowak, Sergey Danilov, Vasco Müller, and Caili Liu

Viewed

Total article views: 2,549 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,854 302 393 2,549 83 118
  • HTML: 1,854
  • PDF: 302
  • XML: 393
  • Total: 2,549
  • BibTeX: 83
  • EndNote: 118
Views and downloads (calculated since 25 Jul 2024)
Cumulative views and downloads (calculated since 25 Jul 2024)

Viewed (geographical distribution)

Total article views: 2,549 (including HTML, PDF, and XML) Thereof 2,495 with geography defined and 54 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Apr 2026
Download
Short summary
A new method called coarse-graining scale analysis is gaining traction as an alternative to Fourier analysis. However, it requires data to be on a regular grid. To address this, we present a high-performance Python package of the coarse-graining technique using discrete Laplacians. This method can handle any mesh type and is ideal for processing output directly from unstructured-mesh models. Computation is split into preparation and solving phases, with GPU acceleration ensuring fast processing.
Read more
Share