Articles | Volume 19, issue 6
https://doi.org/10.5194/gmd-19-2385-2026
https://doi.org/10.5194/gmd-19-2385-2026
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: 3,247 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,462 575 210 3,247 110 187
  • HTML: 2,462
  • PDF: 575
  • XML: 210
  • Total: 3,247
  • BibTeX: 110
  • EndNote: 187
Views and downloads (calculated since 16 Oct 2024)
Cumulative views and downloads (calculated since 16 Oct 2024)

Viewed (geographical distribution)

Total article views: 3,247 (including HTML, PDF, and XML) Thereof 3,247 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 May 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.
Share