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Initiation of deep convection by boundary-layer circulations during TRACER

Active Dates 9/1/2023-8/31/2026
Program Area Atmospheric System Research
Project Description
Initiation of deep convection by boundary-layer circulations during TRACER

Giuseppe Torri, University of Hawai?i at Manoa (Principal Investigator)

Daniel Kirshbaum, McGill University (co-Investigator)

Understanding the transition from shallow cumuli to deep cumulonimbus cloud systems is a major challenge in the study of atmospheric convection. This complex phenomenon involves numerous interrelated processes that often behave non-linearly, making it difficult to fully understand. In addition, the wide range of spatial scales involved further complicates our understanding of this transition. A crucial element in this process is the presence of convergence lines (CLs), regions where different air masses converge. These convergence lines play a critical role in the circulation of the boundary layer over land, facilitating the upward movement of air parcels from the surface to higher altitudes where deep convection can occur. However, while the idea that convergence lines initiate deep convection seems intuitive, the precise relationship between convergence lines and convective updrafts remains an area of active research.

This research proposal aims to address these questions using a multi-angle approach that harnesses the power of idealized and realistic high-resolution simulations, as well as observational data collected during the TRACER field campaign in Houston, Texas, between 2021 and 2022.

The primary goal of this proposal is to characterize the convergence lines observed during the TRACER campaign, focusing on their morphologies and strengths. In doing so, we aim to identify the mechanisms by which convergence lines promote the development of deep convective updrafts. To achieve these goals on the observational side, we will use radar data collected by instruments deployed at the ARM Mobile Facility during TRACER. These data will allow us to track the movement of convergence lines and determine their origin and physical properties. In addition, we will use data from stereo cameras and a geostationary satellite to track and characterize cumulus clouds, thereby establishing clearer relationships between convective clouds and convergence lines.

From a modeling perspective, we will use a combination of realistic and idealized high-resolution simulations to further investigate the objectives of this proposal. Realistic simulations will complement the observational analysis and provide a way to evaluate the results obtained. These simulations will involve performing a similar analysis to that performed on the observational data, as well as developing algorithms to identify and track convergence lines and cumulus clouds based on the model output. On the other hand, idealized simulations will allow us to explore the relationship between convergence lines and deep cumulus clouds in a highly controlled environment. This will provide a solid foundation for the physical interpretation of the main results of this project.

Given the large number of deep convective events associated with convergence lines observed during the TRACER campaign, our analysis will benefit from a wealth of data. In addition, our hypotheses will be tested using state-of-the-art numerical models, including novel Lagrangian diagnostics designed to establish robust cause-effect relationships. We expect that the results of this project will contribute significantly to our understanding of the shallow-to-deep convective transition and to our knowledge of atmospheric moist convection as a whole.
Award Recipient(s)
  • University of Hawaii Honolulu (PI: Torri, Giuseppe)