Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1821-2021
https://doi.org/10.5194/gmd-14-1821-2021
Development and technical paper
 | 
01 Apr 2021
Development and technical paper |  | 01 Apr 2021

Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0

Dana L. McGuffin, Yuanlong Huang, Richard C. Flagan, Tuukka Petäjä, B. Erik Ydstie, and Peter J. Adams

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Cited articles

Aalto, P., Hämeri, K., Becker, E., Weber, R., Salm, J., Mäkelä, J. M., Hoell, C., O'dowd, C. D., Hansson, H.-C., Väkevä, M., Koponen, I. K., Buzorius, G., and Kulmala, M.: Physical characterization of aerosol particles during nucleation events, Tellus B, 53, 344–358, https://doi.org/10.3402/tellusb.v53i4.17127, 2001. 
Aalto, P. and Kulmala, M.: Finland – Hyytiälä (FI0050R) – dmps – particle_number_size_distribution – aerosol [data set], available at: http://ebas.nilu.no/DataSets.aspx?stations=FI0050R&InstrumentTypes=dmps&components=particle_number_size_distribution&fromDate=1970-01-01&toDate=2021-12-31 (last access: 24 March 2021), 2012. 
Adams, P. J. and Seinfeld, J. H.: Predicting global aerosol size distributions in general circulation models, J. Geophys. Res.-Atmos., 107, AAC 4-1–AAC 4-23, https://doi.org/10.1029/2001JD001010, 2002. 
Adams, P. J., Donahue, N. M., and Pandis, S. N.: Atmospheric nanoparticles and climate change, AIChE J., 59, 4006–4019, https://doi.org/10.1002/aic.14242, 2013. 
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Atmospheric particle formation, emissions, and growth process rates are significant sources of uncertainty in predicting climate change. We aim to reduce that uncertainty by using measurements from several ground-based sites across Europe. We developed an estimation technique to adapt the governing process rates so model–measurement bias decays. The estimation framework developed has potential to improve model predictions while providing insight into the underlying atmospheric particle physics.
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