Articles | Volume 20, issue 21
https://doi.org/10.5194/acp-20-12853-2020
https://doi.org/10.5194/acp-20-12853-2020
Research article
 | 
05 Nov 2020
Research article |  | 05 Nov 2020

Using machine learning to derive cloud condensation nuclei number concentrations from commonly available measurements

Arshad Arjunan Nair and Fangqun Yu

Related authors

Particle number concentrations and size distributions in the stratosphere: implications of nucleation mechanisms and particle microphysics
Fangqun Yu, Gan Luo, Arshad Arjunan Nair, Sebastian Eastham, Christina J. Williamson, Agnieszka Kupc, and Charles A. Brock
Atmos. Chem. Phys., 23, 1863–1877, https://doi.org/10.5194/acp-23-1863-2023,https://doi.org/10.5194/acp-23-1863-2023, 2023
Short summary
Wintertime new particle formation and its contribution to cloud condensation nuclei in the Northeastern United States
Fangqun Yu, Gan Luo, Arshad Arjunan Nair, James J. Schwab, James P. Sherman, and Yanda Zhang
Atmos. Chem. Phys., 20, 2591–2601, https://doi.org/10.5194/acp-20-2591-2020,https://doi.org/10.5194/acp-20-2591-2020, 2020
Short summary

Related subject area

Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Revealing dominant patterns of aerosol regimes in the lower troposphere and their evolution from preindustrial times to the future in global climate model simulations
Jingmin Li, Mattia Righi, Johannes Hendricks, Christof G. Beer, Ulrike Burkhardt, and Anja Schmidt
Atmos. Chem. Phys., 24, 12727–12747, https://doi.org/10.5194/acp-24-12727-2024,https://doi.org/10.5194/acp-24-12727-2024, 2024
Short summary
Improving estimation of a record-breaking east Asian dust storm emission with lagged aerosol Ångström exponent observations
Yueming Cheng, Tie Dai, Junji Cao, Daisuke Goto, Jianbing Jin, Teruyuki Nakajima, and Guangyu Shi
Atmos. Chem. Phys., 24, 12643–12659, https://doi.org/10.5194/acp-24-12643-2024,https://doi.org/10.5194/acp-24-12643-2024, 2024
Short summary
Impact of biomass burning aerosols (BBA) on the tropical African climate in an ocean–atmosphere–aerosol coupled climate model
Marc Mallet, Aurore Voldoire, Fabien Solmon, Pierre Nabat, Thomas Drugé, and Romain Roehrig
Atmos. Chem. Phys., 24, 12509–12535, https://doi.org/10.5194/acp-24-12509-2024,https://doi.org/10.5194/acp-24-12509-2024, 2024
Short summary
Retrieval of refractive index and water content for the coating materials of aged black carbon aerosol based on optical properties: a theoretical analysis
Jia Liu, Cancan Zhu, Donghui Zhou, and Jinbao Han
Atmos. Chem. Phys., 24, 12341–12354, https://doi.org/10.5194/acp-24-12341-2024,https://doi.org/10.5194/acp-24-12341-2024, 2024
Short summary
Predicting hygroscopic growth of organosulfur aerosol particles using COSMOtherm
Zijun Li, Angela Buchholz, and Noora Hyttinen
Atmos. Chem. Phys., 24, 11717–11725, https://doi.org/10.5194/acp-24-11717-2024,https://doi.org/10.5194/acp-24-11717-2024, 2024
Short summary

Cited articles

Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Behrens, B., Salwen, C., Springston, S., and Watson, T.: ARM: AOS: aerosol chemical speciation monitor, https://doi.org/10.5439/1046180, 1990. a
Bey, I., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B. D., Fiore, A. M., Li, Q., Liu, H. Y., Mickley, L. J., and Schultz, M. G.: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation, J. Geophys. Res.-Atmos., 106, 23073–23095, https://doi.org/10.1029/2001JD000807, 2001. a, b
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, https://doi.org/10.1007/bf00058655, 1996. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a, b, c
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
Small particles in the atmosphere can affect cloud formation and properties and thus Earth's energy budget. These cloud condensation nuclei (CCN) contribute the largest uncertainties in climate change modeling. To reduce these uncertainties, it is important to quantify CCN numbers accurately, measurements of which are sparse. We propose and evaluate a machine learning method to estimate CCN, in the absence of their direct measurements, using more common measurements of weather and air quality.
Altmetrics
Final-revised paper
Preprint