Clouds have been seeded since the 1940s. Aimed at inducing weather modification, the method uses chemicals to alter the microphysical processes within a cloud to enhance precipitation, suppress hail, or dissipate fog. While the concept of cloud seeding is not new, detailed knowledge about the underlying microphysical processes in clouds is still sparse. CLOUDLAB will tackle this gap by injecting different ice nucleating particle concentrations into supercooled stratus clouds to investigate related microphysical changes, including fundamental aspects of ice formation and growth. To achieve this, the project combines a multi-dimensional approach of targeted cloud seeding with a focus on wintertime stratus clouds in Switzerland, the least dynamic cloud type which best mimics laboratory conditions and allows repetition of field experiments under similar initial conditions. We will inject ice nucleating particles from a seeding drone and perform measurements with ground-based remote sensing, a tethered balloon system equipped with a holographic imager and a measurement drone. Combined with measured meteorological parameters, we aim to understand the impact of cloud inhomogeneities for precipitation initiation via the ice phase. The analysis of our cloud seeding events will be used to validate and improve cloud microphysics schemes in the Swiss weather forecast model in large eddy simulations mode. By coarsening the horizontal resolution to weather forecast mode, the simulations will be extended to include all cloud types where the same microphysical processes are at work to improve precipitation forecast skills in Switzerland. Hence, CLOUDLAB will further the understanding of ice microphysics and precipitation initiation which will also improve climate projections. In addition to pioneering of a new methodology of using drones for cloud seeding, our results will be of particular interest to the weather modification and climate intervention communities.
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