Decoding Deep Convection: Linking Cloud Top Cooling to Environmental Conditions and Storm Evolution
August 12, 2025
Tom Juliano
Committee: Steven Miller (Advisor); Jason Apke (Co-advisor); Kristen Rasmussen; Haonan Chen (Electrical and Computer Engineering)
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Abstract
Forecasting severe deep convection (DC) remains a critical challenge in atmospheric science, particularly during the early stages of storm development when traditional radar-based methods may lack sensitivity. This study explores the use of optical flow-based cloud-top cooling (CTC) as a diagnostic tool for identifying and characterizing DC initiation, environments, and subsequent evolution. By analyzing 1,063 convective events from the 2024 spring storm season from a satellite, radar, and numerical model perspective, this research evaluates the relationship between CTC intensity and environmental instability, as well as the timing of significant and severe storm development. The results demonstrate that stronger CTC signals are generally associated with more unstable atmospheric conditions and are often observed prior to the onset of radar-detectable storm features. These signals tend to precede both the initial development of DC and the emergence of severe weather indicators, such as large hail, by lead times that decrease with increasing CTC magnitude. Integrated CTC metrics, which capture the persistence of cooling over time, further enhance the ability to distinguish between transient and sustained convective systems. While variability exists due to environmental complexity and observational limitations, the findings suggest that CTC offers a meaningful and operationally relevant approach to understanding updraft intensity and near-term evolution. This work contributes to the growing body of research supporting satellite-based nowcasting and highlights the value of CTC in improving short-term detection of hazardous weather. Future efforts will focus on expanding the temporal scope of analysis and integrating additional indicators to refine the predictive capability of CTC-based diagnostics.
