Algorithm for continual monitoring of fog based on geostationary satellite imagery

Jahani, Babak; Karalus, Steffen; Fuchs, Julia; Zech, Tobias; Zara, Marina; Cermak, Jan

doi:https://doi.org/10.5194/amt-18-1927-2025

Articles | Volume 18, issue 8

https://doi.org/10.5194/amt-18-1927-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-18-1927-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 8

Research article

|

29 Apr 2025

Research article |

| 29 Apr 2025

Algorithm for continual monitoring of fog based on geostationary satellite imagery

Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak

Data sets

ASOS-AWOS-METAR Data Download Iowa State University https://mesonet.agron.iastate.edu/request/download.phtml

Model code and software

pytroll/satpy: Version 0.29.0 (v0.29.0) R. Martin et al. https://doi.org/10.5281/zenodo.4904606

XGBoost Documentation xgboost https://xgboost.readthedocs.io/en/stable/index.html

Video supplement

Supplement of "Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting" Babak Jahani et al. https://zenodo.org/records/10244714

Short summary

Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.