Can Glaciogenic Cloud Seeding with a UAV be Utilized to Study Fundamental Aspects of Ice Formation and Growth?

Despite glaciogenic cloud seeding being used operationally to enhance precipitation and suppress hail, the underlying microphysical processes are still poorly characterized. The CLOUDLAB project aims to study the initial formation and subsequent growth of ice crystals by seeding supercooled stratus clouds in combination with numerical simulation of the performed seeding experiments (Henneberger et al., 2023). Many cloud seeding initiatives incorporate scientific evaluation into their operational procedures. In contrast, CLOUDLAB is solely scientifically motivated with the goal of improving precipitation forecasts and projections.

Ice crystal formation in supercooled stratus clouds is triggered by a multirotor UAV carrying a seeding flare that contains silver iodide (Miller et al., 2023). The resulting changes in cloud microphysical properties are then measured 3-10 minutes downstream of the seeding location using balloon-borne in-situ and ground-based remote-sensing instrumentation. Seeding with a multirotor UAV allows us to precisely control the location where seeding particles are released. The persistent nature of stratus clouds enables repeated seeding experiments under similar environmental conditions.

So far, a total of 55 cloud seeding experiments have been conducted over the Swiss Plateau, with seeding temperatures ranging from -10°C to -5°C. High concentrations of aerosol and ice crystals (up to 2000 L-1) were measured simultaneously with enhanced radar reflectivity, while a decrease in cloud droplet concentrations caused by the Wegener-Bergeron-Findeisen process was observed. Ice crystal growth rates were studied by varying the seeding distance between consecutive experiments at similar environmental conditions. Multiple scanning cloud radars provide a measure of the spatial extent of induced changes.

Several seeding experiments were simulated with the numerical weather prediction model ICON in large-eddy mode with robust constraints by the field observations. Simulated radar reflectivity and ice crystal concentrations agree well with the observations when using a freezing parameterization for the seeding particles based on Marcolli et al. (2016) which shows higher activity than the classical parameterizations by DeMott (1995). However, the model fails to reproduce the ice crystal growth rates and the reduction of the cloud droplet concentrations is underestimated. The unique field observations allow a thorough model verification and the constraining of microphysical processes that up to now are based on laboratory measurements.

References:
Henneberger, J. , F. Ramelli, R. Spirig , N. Omanovic, A. J. Miller, C. Fuchs, H. Zhang, J. Bühl, M. Hervo, Z. A. Kanji, . Ohneiser, M. Radenz, M. Rösch, P. Seifert, and U. Lohmann, 2023: Seeding of supercooled low stratus clouds with a UAV to study microphysical ice processes - An introduction to the CLOUDLAB project. BAMS, in press

Miller, A. J., F. Ramelli, C. Fuchs, N. Omanovic, R. Spirig, H. Zhang, U. Lohmann, Z. A. Kanji, and J. Henneberger, 2023: Two new multirotor UAVs for glaciogenic cloud seeding and aerosol measurements within the CLOUDLAB project, AMTD, in review

DeMott, P., 1995: Quantitative descriptions of ice formation mechanisms of silver iodide-type aerosols. Atmospheric Research, 38 (1), 63–99.

Marcolli, C., B. Nagare, A.Welti, and U. Lohmann, 2016: Ice nucleation efficiency of AgI: review and new insights. Atmospheric Chemistry and Physics, 16 (14), 8915–8937.