the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Understanding aerosol microphysical properties from 10 years of data collected at Cabo Verde based on an unsupervised machine learning classification
Heike Wex
Thomas Müller
Silvia Henning
Jens Voigtländer
Alfred Wiedensohler
Frank Stratmann
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The processes that establish how mixed-phase closed-cell clouds transition to more open cellular structures are poorly known. First-of-its kind aircraft observations document such a transition in the presence of anomalously high aerosol concentrations over the Nordic Seas at cloud temperatures < -15 °C. The reduces the drop size, discouraging riming. Eventually, ice precipitation produces surface cold pools that drive the convective transition, despite strong counteracting surface fluxes.
Through the use of our machine-learning-based optical model, realistic BC morphologies can be incorporated into atmospheric science applications that require highly accurate results with minimal computational resources. The results of the study demonstrate that the predictions of single-scattering albedo (ω) and mass absorption cross-section (MAC) were improved over the conventional Mie-based predictions when using the machine learning method.
real-world laboratoryconditions was conducted. We found that measured black carbon (eBC) and particulate matter (PM) in rural shallow terrain depressions with residential wood burning could be much greater than predicted by models. The exceeding levels are a cause for concern since similar conditions can be expected in numerous hilly and mountainous regions across Europe, where approximately 20 % of the total population lives.
hotspotsof interaction. Code and data are open access.