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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. 

Abstract It is well known that African easterly waves (AEWs) can develop into tropical cyclones. However, the processes leading to development are not well understood. To this end, we examine a 38-yr climatology of AEW tracks sorted into developing AEWs (DAEWs) and strong nondeveloping AEWs (SNDAEWs). Wave-centered composites for tracks in the eastern Atlantic (40°–10°W, 5°S–30°N) and West African monsoon regions (10°W–20°E, 5°S–30°N) reveal that DAEWs occur over a more humid background state in both regions. The more humid environment causes DAEWs to exhibit heavier precipitation and wave amplification via vortex stretching. Examination of the column moist static energy (MSE) budget reveals that DAEWs exhibit stronger radiative heating and more moistening via horizontal MSE advection than SNDAEWs. The stronger horizontal MSE advection in DAEWs is due to a northeast shift in the maximum MSE relative to the wave axis, causing the northerlies in the wave to advect a higher MSE into the maximum precipitation. In contrast, MSE is maximum near the center of NDAEWs, making the moistening of the rainfall by horizontal MSE advection weaker. DAEWs exhibit stronger radiative heating per unit of rainfall relative to NDAEWs, suggesting that cloud-radiative feedbacks are stronger in these systems. The sum of horizontal MSE advection and radiative heating explains the buildup in MSE seen over the rainy region of the DAEWs that is not seen in SNDAEWs. These results underscore the importance of moisture, cloud–radiation interactions, and horizontal MSE advection in tropical cyclone (TC) development over these regions. Significance StatementAfrican easterly waves are the most common precursors of tropical cyclones in the Atlantic basin. Despite significant progress in understanding the processes that distinguish waves that develop into tropical cyclones versus those that do not, important gaps in knowledge remain. In this study, we employed a wave-centered compositing scheme and the moist static energy budget to understand the differences between easterly waves that develop and the strongest nondeveloping waves. Our results show that waves that develop into tropical cyclones occur in a more humid environment where less dry air is transported toward the wave’s rainy region. The more humid environment is also associated with stronger rainfall as well as stronger radiative heating in developing waves, the latter which favors the buildup of moisture in developing waves. Our results underscore the importance of water vapor and its horizontal distribution in determining the development of African easterly waves. 

Abstract As one of the most prominent weather systems over the Indian subcontinent, the Indian summer monsoon low pressure systems (MLPSs) have been studied extensively over the past decades. However, the processes that govern the growth of the MLPSs are not well understood. To better understand these processes, we created an MLPS index using bandpass-filtered precipitation data. Lag regression maps and vertical cross sections are used to document the distribution of moisture, moist static energy (MSE), geopotential, and horizontal and vertical motions in these systems. It is shown that moisture governs the distribution of MSE and is in phase with precipitation, vertical motion, and geopotential during the MLPS cycle. Examination of the MSE budget reveals that longwave radiative heating maintains the MSE anomalies against dissipation from vertical MSE advection. These processes nearly cancel one another, and it is variations in horizontal MSE advection that are found to explain the growth and decay of the MSE anomalies. Horizontal MSE advection contributes to the growth of the MSE anomalies in MLPSs prior to the system attaining a maximum amplitude and contributes to decay thereafter. The horizontal MSE advection is largely due to meridional advection of mean state MSE by the anomalous winds, suggesting that the MSE anomalies undergo a moisture–vortex instability (MVI)-like growth. In contrast, perturbation kinetic energy (PKE) is generated through barotropic conversion. The structure, propagation, and energetics of the regressed MLPSs are consistent with both barotropic and moisture–vortex growth.