Detection of Overshooting Tops over East Asia Using Himawari-8

Abstract

Overshooting tops (OTs) are extreme convective cloud tops with strong updraft penetrating into the lower stratosphere. OTs are typically related to surrounding atmospheric conditions, often resulting in extreme weather events such as lightnings, hails, and heavy rainfall. In this study, we used two machine learning approaches-aggregated decision trees(ADT) and logistic regression (LR)-for OT detection to evaluate their performance to see if they result in high detection rates with a significant reduction in false alarms. Himawari-8 data (i.e., 16 channel multispectral images every 10 minute with a resolution of 2 km to 500 m) over East Asia are used in this study. OT reference data were collected using visible channel (0.64 µm) and infrared channel (11.2 µm) images along with MODIS 250 m images through visual interpretation based on the characteristics of OT shape (i.e., dome-like protrusion and anvil cloud with ring-like wave form). A total of 17 variables (i.e., brightness temperature (BT) at 11.2 µm, BT based standard deviations and difference between center and boundary pixels in a series of windows (3x3, 5x5, 7x7, 9x9, 11x11 pixels), and split windows) were extracted based on OT and nonOT references. The hindcast validation results over East Asia showed that ADT performed better than LR, resulting in an average probability of detection (POD) of 70.2 % and an average false alarm rate (FAR) of 49.4 %, while LR results in POD of 50.8 % and FAR of 80.7 %. Through the second post-processing step, which consists of a series of thresholds related to the spatial shape of OTs, FAR was reduced from 49.4 % to 24.5 % with a slight reduction of POD from 70.2% to 65.8 %.