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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2020-281
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-281
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 02 Sep 2020

Submitted as: development and technical paper | 02 Sep 2020

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This preprint is currently under review for the journal GMD.

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

Dana L. McGuffin1, Yuanlong Huang2, Richard C. Flagan2, Tuukka Petäjä3, B. Erik Ydstie4, and Peter J. Adams5 Dana L. McGuffin et al.
  • 1Lawrence Livermore National Laboratory, Livermore, CA, 94550, USA
  • 2Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, 91125, USA
  • 3Institute for Atmospheric and Earth System Research (INAR) / Physics, Faculty of Science, University of Helsinki, Finland
  • 4Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, 15213, USA
  • 5Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, 15213, USA

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 emissions, growth, and nucleation 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 diameter between 3 nm and 6 nm, the number with diameter greater than 10 nm, and the total dry volume of aerosol (N3-6, N10, Vdry), 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 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 Vdry 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.

Dana L. McGuffin et al.

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Dana L. McGuffin et al.

Model code and software

danamcguff/TOMAS-InverseModel: First release used in GMD paper Dana L. McGuffin https://doi.org/10.5281/zenodo.4010877

Dana L. McGuffin et al.

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