Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 1
Articles | Volume 18, issue 1
https://doi.org/10.5194/gmd-18-101-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-101-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 1
Model description paper
 | 
14 Jan 2025
Model description paper |  | 14 Jan 2025

Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data

Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer

Viewed

Total article views: 3,991 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,981 474 1,536 3,991 90 201
  • HTML: 1,981
  • PDF: 474
  • XML: 1,536
  • Total: 3,991
  • BibTeX: 90
  • EndNote: 201
Views and downloads (calculated since 03 Sep 2024)
Cumulative views and downloads (calculated since 03 Sep 2024)

Viewed (geographical distribution)

Total article views: 3,991 (including HTML, PDF, and XML) Thereof 3,888 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Apr 2026
Download
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Read more
Share