Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 6
Articles | Volume 19, issue 6
https://doi.org/10.5194/gmd-19-2385-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-2385-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 6
Development and technical paper
 | 
25 Mar 2026
Development and technical paper |  | 25 Mar 2026

The spatio-temporal visualization tool HMMLVis in renewable energy applications

Rainer Wöß, Kateřina Hlaváčková-Schindler, Irene Schicker, Petrina Papazek, and Claudia Plant

Viewed

Total article views: 1,221 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
902 228 91 1,221 35 61
  • HTML: 902
  • PDF: 228
  • XML: 91
  • Total: 1,221
  • BibTeX: 35
  • EndNote: 61
Views and downloads (calculated since 16 Oct 2024)
Cumulative views and downloads (calculated since 16 Oct 2024)

Viewed (geographical distribution)

Total article views: 1,221 (including HTML, PDF, and XML) Thereof 1,171 with geography defined and 50 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Mar 2026
Download
Short summary
Our tool is an easy-to-use, interpretable causal inference software. It can be applied in any scientific discipline exploring time series. The tool uses heterogeneous Granger causality. It can be used on time-series data to infer causal relationships between multiple variables and a target time-series. The tool is demonstrated on different types of applications related to meteorological events in a renewable energy, air pollution, and postprocessing benchmark data.
Read more
Share