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Abstract The fate of migrating insects that encounter rainfall in flight is a critical consideration when modelling insect movement, but few field observations of this common phenomenon have ever been collected due to the logistical challenges of witnessing these encounters. Operational cloud radars have been deployed around the world by meteorological agencies to study precipitation physics, and as a byproduct, provide a rich database of insect observations that is freely available to researchers. Although considered unwanted ‘clutter’ by the meteorologists who collect the data, the analysis method presented here enables ecologists to delineate co‐occurring signals from insects and raindrops.We present a method that uses image processing techniques on cloud radar velocity spectra to examine the fate of migrating insects when they encounter precipitation. By analysing velocity spectra, we can distinguish flying insects from falling rain and compare the relative density of insects in flight before, during and after the rainfall. We demonstrate the method on a case of insect migration in Oklahoma, USA.Using this method, we show the first reconstructed images of migrating insect layers in flight during rainfall. Our analysis shows that mild to moderate rainfall diminishes the number of insects aloft but does not cause full termination of migratory flight, as has previously been suggested.We hope this technique will spur further investigations of how changing weather conditions impact insect migration, and enable some of the first of such studies in regions of the world that are underrepresented in the literature. 

A common technique for estimating the sea surface generation functions of spray and aerosols is the so-called flux–profile method, where fixed-height concentration measurements are used to infer fluxes at the surface by assuming a form of the concentration profile. At its simplest, this method assumes a balance between spray emission and deposition, and under these conditions the concentration profile follows a power-law shape. It is the purpose of this work to evaluate the influence of waves on this power-law theory, as well as investigate its applicability over a range of droplet sizes. Large-eddy simulations combined with Lagrangian droplet tracking are used to resolve the turbulent transport of spray droplets over moving, monochromatic waves at the lower surface. The wave age and the droplet diameter are varied, and it is found that droplets are highly influenced both by their inertia (i.e., their inability to travel exactly with fluid streamlines) and the wave-induced turbulence. Deviations of the vertical concentration profiles from the power-law theory are found at all wave ages and for large droplets. The dynamics of droplets within the wave boundary layer alter their net vertical fluxes, and as a result, estimates of surface emission based on the flux–profile method can yield significant errors. In practice, the resulting implication is that the flux–profile method may unsuitable for large droplets, and the combined effect of inertia and wave-induced turbulence is responsible for the continued spread in their surface source estimates. 

Abstract The ubiquity of microplastics in marine environments is of growing concern and is increasingly receiving widespread attention. Due to the role of rivers and streams as suppliers of microplastics to the marine environment, it is essential to accurately capture their movements at these scales, but modeling and experimental knowledge in such settings is still limited. In this work, three Markov models, including a continuous time random walk model, Bernoulli model, and spatial Markov model (SMM), are implemented to investigate polyethylene particles transport in open‐channel flows. First, a three‐dimensional high‐resolution direct numerical simulation (DNS) fully resolves a canonical open‐channel flow, and particle transport is simulated using idealized point particles. Then, a series of laboratory transport experiments are conducted in a circulating water tank, and particle image velocimetry methods are used to obtain particle‐tracking data. We find that the correlated Bernoulli model and SMM can successfully reproduce the transport of both DNS and laboratory experiments, particularly in the prediction of measured breakthrough curves, which highlights the importance of correlation between the successive steps. A major benefit of these models is a computational cost that is several orders of magnitude less than, for example, DNS, which demonstrates their high‐efficiency and effectiveness. Therefore, this research offers new insights into the transport of microplastics in open‐channel systems like rivers and streams, which is necessary to prevent and reduce the environmental hazards of microplastics.