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Volume 10, issue 7 | Copyright
Atmos. Meas. Tech., 10, 2613-2626, 2017
https://doi.org/10.5194/amt-10-2613-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 21 Jul 2017

Research article | 21 Jul 2017

Automation and heat transfer characterization of immersion mode spectroscopy for analysis of ice nucleating particles

Charlotte M. Beall1, M. Dale Stokes1, Thomas C. Hill2, Paul J. DeMott2, Jesse T. DeWald3, and Kimberly A. Prather1,4 Charlotte M. Beall et al.
  • 1Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA 92037, USA
  • 2Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, USA
  • 3Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA
  • 4Department of Chemistry, University of California San Diego, La Jolla, CA 92093, USA

Abstract. Ice nucleating particles (INPs) influence cloud properties and can affect the overall precipitation efficiency. Developing a parameterization of INPs in global climate models has proven challenging. More INP measurements – including studies of their spatial distribution, sources and sinks, and fundamental freezing mechanisms – must be conducted in order to further improve INP parameterizations. In this paper, an immersion mode INP measurement technique is modified and automated using a software-controlled, real-time image stream designed to leverage optical changes of water droplets to detect freezing events. For the first time, heat transfer properties of the INP measurement technique are characterized using a finite-element-analysis-based heat transfer simulation to improve accuracy of INP freezing temperature measurement. The heat transfer simulation is proposed as a tool that could be used to explain the sources of bias in temperature measurements in INP measurement techniques and ultimately explain the observed discrepancies in measured INP freezing temperatures between different instruments. The simulation results show that a difference of +8.4°C between the well base temperature and the headspace gas results in an up to 0.6°C stratification of the aliquot, whereas a difference of +4.2°C or less results in a thermally homogenous water volume within the error of the thermal probe, ±0.2°C. The results also show that there is a strong temperature gradient in the immediate vicinity of the aliquot, such that without careful placement of temperature probes, or characterization of heat transfer properties of the water and cooling environment, INP measurements can be biased toward colder temperatures. Based on a modified immersion mode technique, the Automated Ice Spectrometer (AIS), measurements of the standard test dust illite NX are reported and compared against six other immersion mode droplet assay techniques featured in Hiranuma et al. (2015) that used wet suspensions. AIS measurements of illite NX INP freezing temperatures compare reasonably with others, falling within the 5°C spread in reported spectra. The AIS as well as its characterization of heat transfer properties allows higher confidence in accuracy of freezing temperature measurement, allows higher throughput of sample analysis, and enables disentanglement of the effects of heat transfer rates on sample volumes from time dependence of ice nucleation.

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Ice nucleating particles (INPs) influence cloud properties and can affect the overall precipitation efficiency. An existing technique for measuring INP concentrations is modified and automated, and heat transfer properties of the INP measurement technique are characterized for the first time using a finite-element-analysis-based heat transfer simulation to improve accuracy of INP freezing temperature measurement.
Ice nucleating particles (INPs) influence cloud properties and can affect the overall...
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