Unclouding the Correlations: A Principal Component Analysis of Convective Environments

• Published in: Geophysical research letters 2024-12, Vol.51 (24), p.n/a
• Main Authors: Schulte, Richard M., Chase, Randy J., Dolan, Brenda, Marinescu, Peter J., Posselt, Derek J., Rasmussen, Kristen L., Heever, Susan C.
• Format: Article
• Language: English
• Subjects: Complex variables; Convection; Convective storms; Data analysis; environmental analysis; Environmental factors; Life on Earth; precipitation features; principal component analysis; Principal components analysis; Radar; Satellite observation; Satellites; Statistical analysis; Statistical methods; Storms; Winds
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2024GL111732

In this study, we leverage 25 years of observations from spaceborne radars, along with coincident reanalysis data, to determine how the depth and width of precipitating convective storms are related to the large‐scale environments in which they are observed. We find that the deepest convective features are observed in environments markedly different from the environments of other convective features, including organized convection. Deep storms co‐occur with relatively dry, unstable conditions, while wide storms are observed in moist, relatively stable environments. We identify eight large‐scale environmental variables that serve to distinguish between storm modes, and then show that principal component analysis can be used to condense this information into just two scalar variables. The methodology presented offers a succinct way to describe a storm's environment and will allow us to better relate a storm's initial environment to its dynamical characteristics. Plain Language Summary Convective storms are important to understand because of the many ways in which they impact the atmosphere and life on Earth. Our study uses data from satellite missions to look at how large‐scale environmental factors relate to storm characteristics. We match radar observations of storm structures with environmental data like temperature, moisture, and winds. Using a statistical method called principal component analysis, we can then represent these complex environmental patterns in a simplified manner using two main variables. This helps us understand how different types of storms relate to their surroundings. We find that deep storms are often associated with relatively drier, warmer conditions, while wide storms are observed in moister, cooler environments. Using the analytical tools developed here, future research can use new satellite mission observations to better understand storm dynamics and intensity, and their relationship with the environment. Key Points Eight large‐scale environmental variables are identified that are useful for distinguishing between global convective storm modes Principal component analysis can collapse most of the information in these variables into just two dimensions that separate deep storm environments from other storm environments This framework will be useful in the future to connect storm dynamics to storm environments, and to track environmental changes over the lifetime of a convective system