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Abstract
Abstract: Overshooting convective cloud Top (OT)-accompanied clouds can cause severe weather conditions, such as lightning, strong winds, and heavy rainfall. The distribution and behavior of OTs can affect regional and global climate systems. In this paper, we propose a new approach for OT detection by using machine learning methods with multiple infrared images and their derived features. Himawari-8 satellite images were used as the main input data, and binary detection (OT or nonOT) with class probability was the output of the machine learning models. Three machine learning techniques—random forest (RF), extremely randomized trees (ERT), and logistic regression (LR)—were used to develop OT classification models to distinguish OT from non-OT. The hindcast validation over the Southeast Asia and West Pacific regions showed that RF performed best, resulting in a mean probabilities of detection (POD) of 77.06% and a mean false alarm ratio (FAR) of 36.13%. Brightness temperature at 11.2 μm (Tb11) and its standard deviation (STD) in a 3 × 3 window size were identified as the most contributing variables for discriminating OT and nonOT classes. The proposed machine learning-based OT detection algorithms produced promising results comparable to or even better than the existing approaches, which are the infrared window (IRW)-texture and water vapor (WV) minus IRW brightness temperature difference (BTD) methods.
Highlights
Overshooting convective cloud Tops (OTs) are a common phenomenon occurring in strong convective storms over tropical land and ocean regions
OTs were best classified in the extremely randomized trees (ERT) model, which had a peak producer’s accuracy (PA) of 81.40%, followed by random forest (RF)
The nonOT class was well captured by the ERT model with a peak PA and user’s accuracy (UA) of 94.38% and 90.97%, respectively
Summary
Overshooting convective cloud Tops (OTs) are a common phenomenon occurring in strong convective storms over tropical land and ocean regions. OTs, called anvil domes or penetrating tops, are defined as domelike clouds forming above a cumulonimbus cloud top or penetrating tropopause [1]. They form when a rising air parcel in a deep convective cloud penetrates through the equilibrium level (or level of neutral buoyancy) due to the rising parcel’s momentum from strong buoyant updrafts within a thunderstorm. Overshooting deep convective clouds over tropical regions penetrate the tropical tropopause layer and even directly into the lower stratosphere, affecting the budget of heat and constituents [9]. As the effects of OTs on the heat and moisture of the upper troposphere and the lower stratosphere are not yet fully identified [9,10], accurate OT detection and its distribution are crucial to better understand these effects
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