The present study shows the development of the UNAM-MARine Aerosol Tank (UNAM-MARAT), a device that simulates wave breaking to generate marine aerosol particles. The portable and automatic tank is able to generate particle concentrations as high as 2000 cm^-3, covering a wide range of sizes, similar to those found in the ambient marine boundary layer. The sea spray aerosol generated from three natural seawater samples was found to act as ice-nucleating particles (INPs) via immersion freezing.

Airborne particles were i) collected in an agricultural fields and ii) generated in the laboratory from agricultural soil samples to analyze their ice nucleating abilities. It was found that the size and chemical composition of the Mexican agricultural dust particles influence their ice nucleating behavior, where the organic components are likely responsible for their efficiency as INPs. The INP concentrations from the present study are comparable to those from higher latitudes.

The In-Service Aircraft for a Global Observing System (IAGOS) is a small fleet of commercial aircraft that carry a suite of meteorological, gas, aerosol, and cloud sensors and have been measuring worldwide for almost 9 years, since late 2011. Extreme ice events (EIEs) have been identified from the IAGOS cloud measurements and linked to surface emissions for biomass and fossil fuel consumption. The results reported here are highly relevant for climate change and flight operations forecasting.

The representation of the collision–coalescence process in models of different scales has been a great source of uncertainty for many years. The aim of this paper is to show that machine learning techniques can be a useful tool in order to incorporate this process by emulating the explicit treatment of microphysics. Our results show that the machine learning parameterization mimics the evolution of actual droplet size distributions very well.