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Uncertainty in counting ice nucleating particles with continuous flow diffusion chambers

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Garimella Sarvesh, Rothenberg Daniel A., Wolf Martin J., David Robert O., Kanji Zamin A., Wang Chien, Rösch Michael, Cziczo Daniel J.,
Project Elucidating Ice Nucleation Mechanisms Relevant to the Atmosphere: Is deposition nucleation really immersion freezing in pores?
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Original article (peer-reviewed)

Journal Atmospheric Chemistry and Physics
Volume (Issue) 17(17)
Page(s) 10855 - 10864
Title of proceedings Atmospheric Chemistry and Physics
DOI 10.5194/acp-17-10855-2017

Open Access

URL http://doi.org/10.5194/acp-17-10855-2017
Type of Open Access Publisher (Gold Open Access)

Abstract

Abstract. This study investigates the measurement of ice nucleating particle (INP) concentrations and sizing of crystals using continuous flow diffusion chambers (CFDCs). CFDCs have been deployed for decades to measure the formation of INPs under controlled humidity and temperature conditions in laboratory studies and by ambient aerosol populations. These measurements have, in turn, been used to construct parameterizations for use in models by relating the formation of ice crystals to state variables such as temperature and humidity as well as aerosol particle properties such as composition and number. We show here that assumptions of ideal instrument behavior are not supported by measurements made with a commercially available CFDC, the SPectrometer for Ice Nucleation (SPIN), and the instrument on which it is based, the Zurich Ice Nucleation Chamber (ZINC). Non-ideal instrument behavior, which is likely inherent to varying degrees in all CFDCs, is caused by exposure of particles to different humidities and/or temperatures than predicated from instrument theory of operation. This can result in a systematic, and variable, underestimation of reported INP concentrations. We find here variable correction factors from 1.5 to 9.5, consistent with previous literature values. We use a machine learning approach to show that non-ideality is most likely due to small-scale flow features where the aerosols are combined with sheath flows. Machine learning is also used to minimize the uncertainty in measured INP concentrations. We suggest that detailed measurement, on an instrument-by-instrument basis, be performed to characterize this uncertainty.
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