Articles | Volume 18, issue 13
https://doi.org/10.5194/acp-18-9597-2018
https://doi.org/10.5194/acp-18-9597-2018
Research article
 | 
09 Jul 2018
Research article |  | 09 Jul 2018

Identification of new particle formation events with deep learning

Jorma Joutsensaari, Matthew Ozon, Tuomo Nieminen, Santtu Mikkonen, Timo Lähivaara, Stefano Decesari, M. Cristina Facchini, Ari Laaksonen, and Kari E. J. Lehtinen

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Cited articles

Alam, A., Shi Ji, P., and Harrison Roy, M.: Observations of new particle formation in urban air, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2001JD001417, 2003. 
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
Amari, S.-I.: Natural Gradient Works Efficiently in Learning, Neural Comput., 10, 251–276, https://doi.org/10.1162/089976698300017746, 1998. 
Andreae, M. O. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions Part 1, The nature and sources of cloud-active aerosols, Earth-Sci. Rev., 89, 13–41, https://doi.org/10.1016/j.earscirev.2008.03.001, 2008. 
Asmi, E., Kivekäs, N., Kerminen, V.-M., Komppula, M., Hyvärinen, A.-P., Hatakka, J., Viisanen, Y., and Lihavainen, H.: Secondary new particle formation in Northern Finland Pallas site between the years 2000 and 2010, Atmos. Chem. Phys., 11, 12959–12972, https://doi.org/10.5194/acp-11-12959-2011, 2011. 
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Short summary
New particle formation (NPF) in the atmosphere is globally an important source of aerosol particles. NPF events are typically identified and analyzed manually by researchers from particle size distribution data day by day, which is time consuming and might be inconsistent. We have developed an automatic analysis method based on deep learning for NPF event identification. The developed method can be easily utilized to analyze any long-term datasets more accurately and consistently.
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