U‐Net Segmentation for the Detection of Convective Cold Pools From Cloud and Rainfall Fields

Abstract

AbstractConvective cold pools (CPs) mediate interactions between convective rain cells and help organize thunderstorm clusters, in particular mesoscale convective systems and extreme rainfall events. Unfortunately, the observational detection of CPs on a large scale has been hampered by the lack of relevant near‐surface data. Unlike numerical studies, where fields, such as virtual temperature or wind, are available at high resolution and frequently used to detect CPs, observational studies mainly identify CPs based on surface time series, for example, from weather stations or research vessels—thus limiting studies to a regional scope. To expand to a global scope, we here develop and evaluate a methodology for CP detection that relies exclusively on data with (a) global availability and (b) high spatiotemporal resolution. We trained convolutional neural networks to segment CPs in high‐resolution cloud‐resolving simulation output by deliberately restricting ourselves to only cloud top temperature and rainfall fields. Apart from simulations, such data are readily available from geostationary satellites that fulfill both (a) and (b). The networks employ a U‐Net architecture, popular with image segmentation, where spatial correlations at various scales must be learned. Despite the restriction imposed, the trained networks systematically identify CP pixels. Looking ahead, our methodology aims to reliably detect CPs over tropical land from space‐borne sensors on a global scale. As it also provides information on the spatial extent and the relative positioning of CPs over time, our method may unveil the role of CPs in convective organization.