The monitoring of wildfire smoke is important to help mitigate impacts on people such as by sending early warnings to affected areas. Received signal levels (RSLs) from radio links have been used as an opportunistic way to accurately measure rainfall and humidity. Radio links provide integrated measurements along their paths and are an exceptional untapped resource to complement air quality stations in areas affected by smoke events, or in developing countries without air quality monitoring capability. This study analyzed radio link signal fluctuations during smoke events associated with the 2019–2020 Australian wildfires. Concurrently, the atmospheric boundary layer was characterized using atmospheric soundings and surface observations, as well as air quality proxies such as particulate matter concentrations less than 2.5 μm (10 μm), or PM2.5 (PM10). Observations showed that dry air containing large amounts of smoke within a surface layer above the ground acted as a lid, reducing dispersion, trapping and maintaining high ground-level concentrations of smoke. These conditions also created anomalous propagation conditions for radio links and operational weather radars. Power-law relations between signal fluctuations and PM10 and PM2.5 were derived based on the link data collected and the closest air quality station observations. While there was variability in retrieval performance across smoke events, the best performance determination coefficients exceeded 0.5, with an RMSE on the order of less than 50 μg m−3 for concentrations of more than 400 μg m−3. Mid-range link lengths (5–20 km) provided the best results.

Knowledge of the full rainfall drop size distribution (DSD) is critical for characterising liquid water precipitation for applications such as rainfall retrievals using electromagnetic signals and atmospheric model parameterisation. Southern Hemisphere temperate latitudes have a lack of DSD observations and their integrated variables. Laserbased disdrometers rely on the attenuation of a beam by falling particles and are currently the most commonly used type of instrument to observe the DSD. However, there remain questions on the accuracy and variability in the DSDs measured by co-located instruments, whether identical models, different models or from different manufacturers. In this study, raw and processed DSD observations obtained from two of the most commonly deployed laser disdrometers, namely the Parsivel1 from OTT and the Laser Precipitation Monitor (LPM) from Thies Clima, are analysed and compared. Four co-located instruments of each type were deployed over 3 years from 2014 to 2017 in the proximity of Melbourne, a region prone to coastal rainfall in south-eastern Australia. This dataset includes a total of approximately 1.5 million recorded minutes, including over 40 000 min of quality rainfall data common to all instruments, equivalent to a cumulative amount of rainfall ranging from 1093 to 1244mm (depending on the instrument records) for a total of 318 rainfall events. Most of the events lasted between 20 and 40 min for rainfall amounts of 0.12 to 26.0 mm. The colocated LPM sensors show very similar observations, while the co-located Parsivel1 systems show significantly different results. The LPM recorded 1 to 2 orders of magnitude more smaller droplets for drop diameters below 0.6mm compared to the Parsivel1, with differences increasing at higher rainfall rates. The LPM integrated variables showed systematically lower values compared to the Parsivel1. Radar reflectivity- rainfall rate (ZH-R) relationships and resulting potential errors are also presented. Specific ZH-R relations for drizzle and convective rainfall are also derived based on DSD collected for each instrument type. Variability of the DSD as observed by co-located instruments of the same manufacturer had little impact on the estimated ZH-R relationships for stratiform rainfall, but differs when considering convective rainfall relations or ZH-R relations fitted to all available data. Conversely, disdrometer-derived ZH-R relations as compared to the Marshall-Palmer relation ZH D 200R1:6 led to a bias in rainfall rates for reflectivities of 50 dBZ of up to 21.6mmh1. This study provides an open-source high-resolution dataset of co-located DSD to further explore sampling effects at the micro scale, along with rainfall microstructure.