The main field activities of the Coordinated Airborne Studies in the Tropics (CAST) campaign took place in the west Pacific during January–February 2014. The field campaign was based in Guam (13.5°N, 144.8°E), using the U.K. Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 atmospheric research aircraft, and was coordinated with the Airborne Tropical Tropopause Experiment (ATTREX) project with an unmanned Global Hawk and the Convective Transport of Active Species in the Tropics (CONTRAST) campaign with a Gulfstream V aircraft. Together, the three aircraft were able to make detailed measurements of atmospheric structure and composition from the ocean surface to 20 km. These measurements are providing new information about the processes influencing halogen and ozone levels in the tropical west Pacific, as well as the importance of trace-gas transport in convection for the upper troposphere and stratosphere. The FAAM aircraft made a total of 25 flights in the region between 1°S and 14°N and 130° and 155°E. It was used to sample at altitudes below 8 km, with much of the time spent in the marine boundary layer. It measured a range of chemical species and sampled extensively within the region of main inflow into the strong west Pacific convection. The CAST team also made ground-based measurements of a number of species (including daily ozonesondes) at the Atmospheric Radiation Measurement Program site on Manus Island, Papua New Guinea (2.1°S, 147.4°E). This article presents an overview of the CAST project, focusing on the design and operation of the west Pacific experiment. It additionally discusses some new developments in CAST, including flights of new instruments on board the Global Hawk in February–March 2015.

Special issue on XI Conference on Electromagnetic and Light Scattering by Non-Spherical Particles: 2008. Original article can be found at: http://www.sciencedirect.com/science/journal/00224073 Copyright Elsevier B.V. DOI: 10.1016/j.jqsrt.2009.01.011The applicability of the ray tracing with diffraction on facets model is extended to particles with curved surfaces. This allows tests against T-matrix calculations for larger size parameters and modelling of light scattering by more realistic particle shapes, such as ice crystals with rounded edges

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.

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.

We present results from a study evaluating the utility of supervised machine learning to classify single particle ultraviolet laser-induced fluorescence (UV-LIF) signatures to investigate airborne primary biological aerosol particle (PBAP) concentrations in a busy, multifunctional building using a Multiparameter Bioaerosol Spectrometer. First we introduce and demonstrate a gradient boosting ensemble decision tree algorithm’s ability to accurately classify laboratory generated PBAP samples into broad taxonomic classes with a high level of accuracy. We then develop a framework to appraise the classification accuracy and performance using the Hellinger distance metric to compare product parameter probability density function similarity; this framework showed that key training classes were sufficiently different in terms of particle fluorescence and morphology to facilitate classification. We also demonstrate the utility of including advanced morphological parameters to minimise inter-class conflation and improve classification confidence, where relying on the fluorescent spectra alone would likely result in misattribution. Finally, we apply these methods to ambient data collected within a large multi-functional building where ambient bacterial- and fungal-like classes were identified to display trends corresponding to human activity; fungal-like classes displayed a consistent diurnal trend with a maximum at midday and hourly peaks correlating to movements within the building; bacteria-like aerosol displayed complex, episodic events during opening hours. All PBAP classes fell to low baseline concentrations when the building was unoccupied overnight and at weekends.