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Abstract While considerable attention has been given to how convectively coupled Kelvin waves (CCKWs) influence the genesis of tropical cyclones (TCs) in the Atlantic Ocean, less attention has been given to their direct influence on African easterly waves (AEWs). This study builds a climatology of AEW and CCKW passages from 1981 to 2019 using an AEW-following framework. Vertical and horizontal composites of these passages are developed and divided into categories based on AEW position and CCKW strength. Many of the relationships that have previously been found for TC genesis also hold true for non-developing AEWs. This includes an increase in convective coverage surrounding the AEW center in phase with the convectively enhanced (“active”) CCKW crest, as well as a buildup of relative vorticity from the lower to upper troposphere following this active crest. Additionally, a new finding is that CCKWs induce specific humidity anomalies around AEWs that are qualitatively similar to those of relative vorticity. These modifications to specific humidity are more pronounced when AEWs are at lower latitudes and interacting with stronger CCKWs. While the influence of CCKWs on AEWs is mostly transient and short lived, CCKWs do modify the AEW propagation speed and westward-filtered relative vorticity, indicating that they may have some longer-term influences on the AEW life cycle. Overall, this analysis provides a more comprehensive view of the AEW–CCKW relationship than has previously been established, and supports assertions by previous studies that CCKW-associated convection, specific humidity, and vorticity may modify the favorability of AEWs to TC genesis over the Atlantic.
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This content will become publicly available on December 7, 2026
Machine Learning Approaches to Identifying Tropical Waves That Develop into Hurricanes
African Easterly Waves (AEWs) are synoptic-scale atmospheric disturbances that serve as precursors to tropical cyclones (TCs) in the North Atlantic and North Africa. As climate changes, TC activities are increasingly frequent, leading to exponentially growing socio-economic losses. So understanding the physical mechanisms governing the tropical cyclogenesis (TCG) of AEWs remains a crucial problem. Competing theoretical frameworks, including baroclinic instability, barotropic instability, and moisture-vortex instability (MVI) have been proposed, but their relative importance and temporal evolution during storm development remain unclear. In this study, machine learning algorithms are used to empirically analyze the governing mechanisms of AEW development based on 40 years of reanalysis data (1979-2018). We develop a computer vision framework utilizing convolutional neural networks (CNNs) and transformer architectures to identify developing AEWs (DAEWs) from non-developing AEWs (NDAEWs) based on wave-centered composites of key thermodynamic and dynamic variables for storm development. The model results suggest that the MVI framework is a critical factor for high classification accuracy in distinguishing developers from non-developers.
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- Award ID(s):
- 2411723
- PAR ID:
- 10658032
- Publisher / Repository:
- NeurIPS Climate Change AI Workshop
- Date Published:
- Format(s):
- Medium: X
- Location:
- San Diego, CA
- Sponsoring Org:
- National Science Foundation
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