Articles | Volume 18, issue 11
https://doi.org/10.5194/amt-18-2311-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2311-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jun 2025
Research article |
| 03 Jun 2025
| 03 Jun 2025
Convolutional neural networks for specific and merged data sets of optical array probe images: compatibility of retrieved morphology-dependent size distributions
Louis Jaffeux
CORRESPONDING AUTHOR
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
Jan Breiner
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
Pierre Coutris
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
Alfons Schwarzenböck
Laboratoire de Météorologie Physique (UMR6016)/UCA/CNRS, Aubière, France
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Short summary
Airborne cloud observation relies on high-frequency black-and-white-image information. The study presents automatic shape recognition tools developed with machine learning techniques and adapted for this type of image. Applied on a recent field campaign, these tools produce morphology-specific size distributions that can be compared across four instruments covering different size ranges. The analysis show that the tools are performing well and are consistent across the different instruments.
Airborne cloud observation relies on high-frequency black-and-white-image information. The study...
