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Study Uses Large-eddy Simulation Model to Investigate Hygroscopic Cloud Seeding Effect on Rainfall

Water scarcity has become a major challenge for arid and semi-arid regions across the globe, sparking a renewed interest in rain enhancement research and investments, particularly in countries at high risk of water stress. One of the most widely used rain enhancement methods is introducing large hygroscopic particles into the cloud, which act as Cloud Condensation Nuclei (CCN) and enhance the growth of droplets and the subsequent production of drizzle and precipitation.

Despite numerous experiments performed on hygroscopic cloud seeding, estimates of its effect on rainfall are still somewhat uncertain. This is because field experiments only provide a single realization of an event: as soon as the cloud is seeded, the reference point is lost, making the reproducibility of results from cloud seeding field experiments a challenging task.

Modelling studies, on the other hand, provide the advantage of generating multiple realizations of each scenario and, with a carefully planned setup, the experiments are reproducible. Cloud-resolving models comprise an important source of information to complement field campaign studies, as they provide a highly controlled environment for repeatable experiments on the seeding efficacy, which helps to tackle the attribution issue.

Professor Hannele Korhonen, the Director of the Climate Research Program at Finnish Meteorological Institute and a second cycle awardee of the UAE Research Program for Rain Enhancement Science (UAEREP) conducted such a study employing a cloud resolving large-eddy model that offered a robust method to detect and attribute the seeding effects.

The study investigated artificial enhancement of precipitation through hygroscopic cloud seeding with a numerical large-eddy simulation model called the UCLALES-SALSA and a spectral aerosol-cloud microphysics module. The investigation focused on marine stratocumulus clouds, which arguably provide the simplest environment to analyze the governing microphysical processes, and evaluated its model results by comparing them with recently published results from field observations.

Owing to the detailed representation of aerosol-cloud interactions, the model successfully reproduced the microphysical signatures attributed to the seeding, that were also seen in the observations. Moreover, the model simulations showed up to a 2-3 fold increase in the precipitation flux due to the seeding, depending on the seeding rate and injection strategy.

However, the simulations suggest that a relatively high seeding particle emission rate is needed for a substantial increase in the precipitation yield, as compared with the estimated seeding concentrations from the field campaign. In practical applications, the seeding aerosol is often produced by flare burning.

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