larda_cloud_sniffer

Identify coherent cloud objects in the Cloudnet classification and extract characteristic properties from layered mixed-phase clouds.

The structure of the folders is as following:

sniffer_code

• the cloud sniffer is originally based on Bühl [2016 ACP]
• adapted for the new datasets, additinal measurements, new server and especially larda3.

cloud_properties

.dat files produced by python3 cc_sniffer_ac.py --campaign lacros_dacapo --date 20181128 for a single day. Contains all the features (range chunks of profiles) connected into clouds.

cloud_collections

Statistics of the single cloud features for the full campaign in a .csv table. Created by python3 cc_collector_ac.py --campaign lacros_dacapo --date 20181128

The cloud collections for the LACROS campaigns at Leipzig, Limassol and Punta Arenas are included in the zenodo repository, but not in the github repository. To obtain those git clone this repository and then get the .csv files from the zenodo repository.

analysis_code

Collection of ipython notebooks to generate the analysis and the plots used in the recent publication.

References

• Bühl, J., Seifert, P., Myagkov, A., and Ansmann, A.: Measuring ice- and liquid-water properties in mixed-phase cloud layers at the Leipzig Cloudnet station, Atmos. Chem. Phys., 16, 1060-10620, https://doi.org/10.5194/acp-16-10609-2016, 2016.
• Radenz, M., Bühl, J., Seifert, P., Baars, H., Engelmann, R., Barja González, B., Mamouri, R.-E., Zamorano, F., and Ansmann, A.: Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: Disentangling the role of aerosol and dynamics with ground-based remote sensing, Atmos. Chem. Phys. Discuss. [accepted for publication], https://doi.org/10.5194/acp-2021-360, 2021.

License

See the LICENSE file for more information Copyright 2021, Martin Radenz MIT License