Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3715-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-3715-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Jun 2021
Development and technical paper |
| 22 Jun 2021
| 22 Jun 2021
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Aku Seppänen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Jari P. Kaipio
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Department of Mathematics, Faculty of Science, University of Auckland, Auckland, New Zealand
Kari E. J. Lehtinen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Finnish Meteorological Institute, Kuopio, Finland
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Short summary
Experimental research has provided large amounts of high-quality data on aerosol over the last 2 decades. However, inference of the process rates (e.g., the rates at which particles are generated) is still typically done by simple curve-fitting methods and does not assess the credibility of the estimation. The devised method takes advantage of the Bayesian framework to not only retrieve the state of the observed aerosol system but also to estimate the process rates (e.g., growth rate).
Experimental research has provided large amounts of high-quality data on aerosol over the last 2...
