Wednesday, 30 August 2023: 9:45 AM
Great Lakes BC (Hyatt Regency Minneapolis)
Cloud-top generating cells are fine-scale structures that frequently appear in various cloud systems, such as wintertime orographic clouds, Southern Ocean clouds, and winter cyclones. These structures are hypothesized to create favorable conditions for the formation and growth of natural ice, which play a crucial role in precipitation production. However, identifying and understanding cloud-top generating cells remains challenging in both observations and simulations, primarily due to an incomplete comprehension of their formation mechanisms and the associated microphysical and dynamical properties.
To better understand the structure and characteristics of generating cells, we conducted a WRF 20-m LES simulation of the winter orographic clouds during SNOWIE (Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment). The Cloud Resolving Model Radar Simulator (CR-SIM) was used to simulate the vertically-pointing cloud radar signal along the flight track near the cloud top. CR-SIM can transform the model geophysical quantities into radar signals and offers a commensurate, apples-to-apples comparison between model simulation and radar observations. We apply and compare different generating cell identification methods while jointly analyzing radar observables from the radar simulator, such as spectrum width and radar reflectivity, with the associated microphysical and dynamical fields obtained from LES. This analysis generates a unique dataset that reveals the key processes and physical properties of generating cells.
To better understand the structure and characteristics of generating cells, we conducted a WRF 20-m LES simulation of the winter orographic clouds during SNOWIE (Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment). The Cloud Resolving Model Radar Simulator (CR-SIM) was used to simulate the vertically-pointing cloud radar signal along the flight track near the cloud top. CR-SIM can transform the model geophysical quantities into radar signals and offers a commensurate, apples-to-apples comparison between model simulation and radar observations. We apply and compare different generating cell identification methods while jointly analyzing radar observables from the radar simulator, such as spectrum width and radar reflectivity, with the associated microphysical and dynamical fields obtained from LES. This analysis generates a unique dataset that reveals the key processes and physical properties of generating cells.
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