Automation and Heat Transfer Characterization of Immersion Mode Spectroscopy for Analysis of Ice Nucleating Particles

Abstract

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 +5.6 °C between the well base temperature and the headspace gas results in an up to 3 °C stratification of the aliquot, whereas a difference of +3.0 °C or less results in a thermally homogenous water volume within ±0.05 °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. Using the modified immersion mode technique, the Automated Ice Spectrometer (AIS), measurements of the standard test dust illite NX are reported and compared against 5 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 ºC spread in reported spectra. The AIS and its characterization of heat transfer properties allows higher confidence in accuracy of freezing temperature measurement, higher throughput of sample analysis, and enables disentanglement of the effects of heat transfer rates on sample volumes from time dependence of ice nucleation.

Comment on acp-2020-1229

“…The automated sampler would engage when triggered by precipitation of at least 0.13 cm h -1 and would sample using the first of 24 bottles for 30 minutes, and thereafter switch bottles at hourly intervals. Within one to two hours of sample collection, INP concentrations were measured using the SIO-Automated Ice Spectrometer (SIO-AIS) (Beall et al, 2017), an automated offline freezing assay technique for measurement of immersion mode INPs. To decrease the effect of interstitial particle sweep out by falling raindrops on measured INP concentration, precipitation from the first 30 minutes was discarded.…”

“…Sweep out effects have been estimated to contribute between 1.2 and 14 % to measured concentrations of INP in a precipitation sample (Wright et al, 2014). The INP measurement technique is described in detail in (Beall et al, 2017). Briefly, the precipitation samples were distributed in microliter aliquots into a clean 96-well disposable polypropylene sample tray.…”

“…S1c). and Coull, 1998) and any ZoBell confidence interval within ± 2.2 °C, the maximum uncertainty in freezing temperature measurement due to heterogeneity in heat transfer rates across the instrument's droplet assay (Beall et al, 2017). This equates to a significance threshold of p < 0.005 (Krzywinski and Altman, 2013).…”

Ice-Nucleating Particles Near Two Major Dust Source Regions

“…INP concentrations were measured using the SIO-Automated Ice Spectrometer (SIO-AIS), an immersion freezing droplet assay instrument that is described in detail in Beall et al (2017).…”