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

McGuffin, Dana L. and Huang, Yuanlong and Flagan, Richard C. and Petäjä, Tuukka and Ydstie, B. Erik and Adams, Peter J. (2021) Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0. Geoscientific Model Development, 14 (3). pp. 1821-1839. ISSN 1991-9603. doi:10.5194/gmd-14-1821-2021. https://resolver.caltech.edu/CaltechAUTHORS:20210506-152125131

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Abstract

Atmospheric aerosol microphysical processes are a significant source of uncertainty in predicting climate change. Specifically, aerosol nucleation, emissions, and growth rates, which are simulated in chemical transport models to predict the particle size distribution, are not understood well. However, long-term size distribution measurements made at several ground-based sites across Europe implicitly contain information about the processes that created those size distributions. This work aims to extract that information by developing and applying an inverse technique to constrain aerosol emissions as well as nucleation and growth rates based on hourly size distribution measurements. We developed an inverse method based upon process control theory into an online estimation technique to scale aerosol nucleation, emissions, and growth so that the model–measurement bias in three measured aerosol properties exponentially decays. The properties, which are calculated from the measured and predicted size distributions, used to constrain aerosol nucleation, emission, and growth rates are the number of particles with a diameter between 3 and 6 nm, the number with a diameter greater than 10 nm, and the total dry volume of aerosol (N₃₋₆, N₁₀, V_(dry)), respectively. In this paper, we focus on developing and applying the estimation methodology in a zero-dimensional “box” model as a proof of concept before applying it to a three-dimensional simulation in subsequent work. The methodology is first tested on a dataset of synthetic and perfect measurements that span diverse environments in which the true particle emissions, growth, and nucleation rates are known. The inverse technique accurately estimates the aerosol microphysical process rates with an average and maximum error of 2 % and 13 %, respectively. Next, we investigate the effect that measurement noise has on the estimated rates. The method is robust to typical instrument noise in the aerosol properties as there is a negligible increase in the bias of the estimated process rates. Finally, the methodology is applied to long-term datasets of in situ size distribution measurements in western Europe from May 2006 through June 2007. At Melpitz, Germany, and Hyytiälä, Finland, the average diurnal profiles of estimated 3 nm particle formation rates are reasonable, having peaks near noon local time with average peak values of 1 and 0.15 cm⁻³ s⁻¹, respectively. The normalized absolute error in estimated N₃₋₆, N₁₀, and V_(dry) at three European measurement sites is less than 15 %, showing that the estimation framework developed here has potential to decrease model–measurement bias while constraining uncertain aerosol microphysical processes.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.5194/gmd-14-1821-2021DOIArticle
https://doi.org/10.5194/gmd-14-1821-2021-supplementDOISupplement
ORCID:
AuthorORCID
McGuffin, Dana L.0000-0002-5932-8372
Huang, Yuanlong0000-0002-6726-8904
Flagan, Richard C.0000-0001-5690-770X
Petäjä, Tuukka0000-0002-1881-9044
Additional Information:© Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Received: 22 August 2020 – Discussion started: 2 September 2020; Revised: 14 January 2021 – Accepted: 10 February 2021 – Published: 1 April 2021. The authors acknowledge Thomas Tuch, Pasi Aalto, Stefano Decesari, and Jorma Joutsensaari for providing descriptions and technical specifications of their measurement setup. This work was performed partially under the auspices of the US Department of Energy by the Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 with IM release number LLNL-JRNL-813683. We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu, last access: 24 March 2021) and the data providers in the ECA&D project (https://www.ecad.eu, last access: 24 March 2021). This research was supported by the Carnegie Mellon University Department of Chemical Engineering and the Mahmood I. Bhutta Fellowship in Chemical Engineering. Tuukka Petäjä acknowledges financial support through the Academy of Finland (304347), the Center of Excellence in Atmospheric Sciences, and the European Commission through the Horizon 2020 research and innovation program under grant agreement no. 689443 via project iCUPE (Integrative and Comprehensive Understanding on Polar Environments). The supplement related to this article is available online at: https://doi.org/10.5194/gmd-14-1821-2021-supplement. Author contributions: PJA, BEY, and DLM conceptualized the simulations and estimation technique. DLM adapted the model code with the estimation algorithm and performed all simulations. RF and YH conceptualized the measurement uncertainty. YH created the uncertainty code. DLM authored the text with contributions from all co-authors. YH wrote Appendix A. The authors declare that they have no conflict of interest. Review statement: This paper was edited by Christina McCluskey and reviewed by three anonymous referees.
Funders:
Funding AgencyGrant Number
Carnegie Mellon UniversityUNSPECIFIED
Academy of Finland304347
European Research Council (ERC)689443
Issue or Number:3
DOI:10.5194/gmd-14-1821-2021
Record Number:CaltechAUTHORS:20210506-152125131
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210506-152125131
Official Citation:McGuffin, D. L., Huang, Y., Flagan, R. C., Petäjä, T., Ydstie, B. E., and Adams, P. J.: Novel estimation of aerosol processes with particle size distribution measurements: a case study with the TOMAS algorithm v1.0.0, Geosci. Model Dev., 14, 1821–1839, https://doi.org/10.5194/gmd-14-1821-2021, 2021
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108996
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 May 2021 22:57
Last Modified:06 May 2021 22:57

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