Cloud Particle Phase Determination using a High-Speed Particle Phase Discriminator

Mixed-phase clouds (MPCs) are composed of both supercooled liquid droplets and ice crystals. They are thermodynamically instable as a result of the lower saturation vapor pressure over ice as compared to liquid water. A key reason to understand the complex microphysical processes in MPCs is to accurately represent different hydrometeor types and the corresponding phase partitioning of water. Phase discrimination, quantification of hydrometeor type and determination of their size distribution is essential for many processes including cloud lifetime and radiative properties as well as precipitation formation. Precipitation formation in turn is mainly initiated through the ice phase [1, 2] highlighting the need for ice nucleation studies encompassing hydrometeor phase discrimination. Laboratory studies investigating formation of primary ice crystals under MPC conditions include the use of Continuous Flow Diffusion Chamber (CFDC) experiments. Many CFDC studies use conventional optical particle counters, where particle phase is solely inferred from optical particle size.

Here, we present a new instrument, the high-speed Particle Phase Discriminator (PPD-HS), designed to determine the phase of cloud particles. We present in-depth analysis of instrumental performance and verify its applicability to CFDC experiments when operated as a particle detector at thermodynamic conditions relevant for MPCs.

PPD-HS records near-forward spatial intensity distributions along two vertically oriented CMOS (Complementary Metal- Oxide-Semiconductor) arrays, aligned with the optical axis of light scattered by individual particles. The unique aspect of PPD-HS compared to its predecessors [3] is the reduction of the memory bandwidth by only recording one-dimensional slices of the scattered light pattern. This significantly improves its sampling rate, allowing for detection of more than 1200 particles per second, making it suitable for laboratory applications. The intensity of scattered light is primarily a function of particle size and shape as well as polarization and wavelength of the light source [4]. We show that even the reduced intensity profiles encompass enough information on particle morphology to discriminate particles as either spherical cloud droplets or aspherical ice crystals. This is achieved through symmetry analysis of the recorded scattering pattern, which are based on formal measures, that have been carefully developed within the framework of this project. We quantify the phase discrimination capabilities of PPD-HS through a series of benchmark experiments. We therefore use a variety of monodisperse particle populations with well-controlled shape and size, ranging from 1 to 15 mum. This is a common size range of particles generated within CFDCs.

Our results show that PPD-HS successfully discriminates particle phase with a maximum misclassification ratio below 11%. Initial experiments reveal that incorporating PPD-HS into a CFDC setup extends the thermodynamic range in which particle phase can be reliably determined as compared to using regular optical particle Counters.

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