We present the first study of extended field observations made by a network of 80 automatic hail sensors from Switzerland. The sensors record the exact timing of hailstone impacts, providing valuable information about the local duration of hailfall. We found that the majority of hailfalls lasts just a few minutes and that most hailstones, including the largest, fall during a first phase of high hailstone density, while a few remaining and smaller hailstones fall in a second low-density phase.

This study tests the new water condensation particle counter (MAGIC 210-LP) for deployment on passenger aircraft coordinated by the European research infrastructure IAGOS. We conducted a series of laboratory experiments for flight altitude conditions. We demonstrate that this water condensation particle counter model shows excellent agreement with a butanol-based instrument used in parallel and a Faraday cup electrometer as reference instrument at all tested pressure conditions.

Organic sulfur compounds are used to identify sources and atmospheric processing of aerosol. Our paper evaluates the potential of using a non-destructive measurement technique to measure organic sulfur compounds in filter samples by assessing their chemical stability over time. Some were stable, but some evaporated or changed chemically. Future work includes evaluating the stability and potential interference of multiple organic sulfur compounds in laboratory mixtures and ambient aerosol.

In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.

Data collected using air quality instrumentation deployed on automobiles and driven repeatedly in Houston neighborhoods are analyzed using a novel machine learning technique. The aim is to separate large plumes from the rest of the data in order to identify the sources of the highest levels of the pollutants. The number and nature of these plumes are characterized spatially and can be linked to emissions from different types of motor vehicles.