Abstract. A low-cost miniaturized particle counter has been developed by The University of Hertfordshire (UH) for the measurement of aerosol and droplet concentrations and size distributions. The Universal Cloud and Aerosol Sounding System (UCASS) is an optical particle counter (OPC), which uses wide-angle elastic light scattering for the high-precision sizing of fluid-borne particulates. The UCASS has up to 16 configurable size bins, capable of sizing particles in the range 0.4–40 µm in diameter. Unlike traditional particle counters, the UCASS is an open-geometry system that relies on an external air flow. Therefore, the instrument is suited for use as part of a dropsonde, balloon-borne sounding system, as part of an unmanned aerial vehicle (UAV), or on any measurement platform with a known air flow. Data can be logged autonomously using an on-board SD card, or the device can be interfaced with commercially available meteorological sondes to transmit data in real time. The device has been deployed on various research platforms to take measurements of both droplets and dry aerosol particles. Comparative results with co-located instrumentation in both laboratory and field settings show good agreement for the sizing and counting ability of the UCASS.

Copyright 2015 The Authors. Published by Elsevier Ltd.This is an open access article under the CC-BY license (http://creativecommons.org/licenses/by/4.0/). Date of Acceptance: 16/02/2015Measurements are presented of the phase function, P11, and asymmetry parameter, g, of five ice clouds created in a laboratory cloud chamber. At ???7 ??C, two clouds were created: one comprised entirely of solid columns, and one comprised entirely of hollow columns. Similarly at ???15 ??C, two clouds were created: one consisting of solid plates and one consisting of hollow plates. At ???30 ??C, only hollow particles could be created within the constraints of the experiment. The resulting cloud at ???30 ??C contained short hollow columns and thick hollow plates. During the course of each experiment, the cloud properties were monitored using a Cloud Particle Imager (CPI). In addition to this, ice crystal replicas were created using formvar resin. By examining the replicas under an optical microscope, two different internal structures were identified. The internal and external facets were measured and used to create geometric particle models with realistic internal structures. Theoretical results were calculated using both Ray Tracing (RT) and Ray Tracing with Diffraction on Facets (RTDF). Experimental and theoretical results are compared to assess the impact of internal structure on P11 and g and the applicability of RT and RTDF for hollow columns

Abstract. Small unmanned aircraft (SUA) have the potential to be used as platforms for the measurement of atmospheric particulates. The use of an SUA platform for these measurements provides benefits such as high manoeuvrability, reusability, and low cost when compared with traditional techniques. However, the complex aerodynamics of an SUA – particularly for multi-rotor airframes – pose difficulties for accurate and representative sampling of particulates. The use of a miniaturised, lightweight optical particle instrument also presents reliability problems since most optical components in a lightweight system (for example laser diodes, plastic optics, and photodiodes) are less stable than their larger, heavier, and more expensive equivalents (temperature-regulated lasers, glass optics, and photomultiplier tubes). The work presented here relies on computational fluid dynamics with Lagrangian particle tracking (CFD–LPT) simulations to influence the design of a bespoke meteorological sampling system: the UH-AeroSAM. This consists of a custom-built airframe, designed to reduce sampling artefacts due to the propellers, and a purpose-built open-path optical particle counter (OPC) – the Ruggedised Cloud and Aerosol Sounding System (RCASS). OPC size distribution measurements from the UH-AeroSAM are compared with the cloud, aerosol, and precipitation spectrometer (CAPS) for measurements of stratus clouds during the Pallas Cloud Experiment (PaCE) in 2019. Good agreement is demonstrated between the two instruments. The integrated dN∕dlog (Dp) is shown to have a coefficient of determination of 0.8 and a regression slope of 0.9 when plotted 1:1.

?? 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).Ice clouds were generated in the Manchester Ice Cloud Chamber (MICC), and the backscattering linear depolarisation ratio, ??, was measured for a variety of habits. To create an assortment of particle morphologies, the humidity in the chamber was varied throughout each experiment, resulting in a range of habits from the pristine to the complex. This technique was repeated at three temperatures: ???7 ??C, ???15 ??C and ???30 ??C, in order to produce both solid and hollow columns, plates, sectored plates and dendrites. A linearly polarised 532 nm continuous wave diode laser was directed through a section of the cloud using a non-polarising 50:50 beam splitter. Measurements of the scattered light were taken at 178??, 179?? and 180??, using a Glan???Taylor prism to separate the co- and cross-polarised components. The intensities of these components were measured using two amplified photodetectors and the ratio of the cross- to co-polarised intensities was measured to find the linear depolarisation ratio. In general, it was found that Ray Tracing over-predicts the linear depolarisation ratio. However, by creating more accurate particle models which better represent the internal structure of ice particles, discrepancies between measured and modelled results (based on Ray Tracing) were reduced

A new instrument, the High-speed Particle Phase Discriminator (PPD-HS), developed at the University of Hertfordshire, for sizing individual cloud hydrometeors and determining their phase is described herein. PPD-HS performs an in situ analysis of the spatial intensity distribution of near-forward scattered light for individual hydrometeors yielding shape properties. Discrimination of spherical and aspherical particles is based on an analysis of the symmetry of the recorded scattering patterns. Scattering patterns are collected onto two linear detector arrays, reducing the complete 2-D scattering pattern to scattered light intensities captured onto two linear, one-dimensional strips of light sensitive pixels. Using this reduced scattering information, we calculate symmetry indicators that are used for particle shape and ultimately phase analysis. This reduction of information allows for detection rates of a few hundred particles per second.Here, we present a comprehensive analysis of instrument performance using both spherical and aspherical particles generated in a well-controlled laboratory setting using a vibrating orifice aerosol generator (VOAG) and covering a size range of approximately 3-32 µm. We use supervised machine learning to train a random forest model on the VOAG data sets that can be used to classify any particles detected by PPD-HS. Classification results show that the PPD-HS can successfully discriminate between spherical and aspherical particles, with misclassification below 5 % for diameters > 3 µm. This phase discrimination method is subsequently applied to classify simulated cloud particles produced in a continuous flow diffusion chamber setup. We report observations of small, near-spherical ice crystals at early stages of the ice nucleation experiments, where shape analysis fails to correctly determine the particle phase. Nevertheless, in the case of simultaneous presence of cloud droplets and ice crystals, the introduced particle shape indicators allow for a clear distinction between these two classes, independent of optical particle size. From our laboratory experiments we conclude that PPD-HS constitutes a powerful new instrument to size and discriminate the phase of cloud hydrometeors. The working principle of PPD-HS forms a basis for future instruments to study microphysical properties of atmospheric mixed-phase clouds that represent a major source of uncertainty in aerosol-indirect effect for future climate projections.