Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data

Sanggyun Lee,Eunna Jang,Hyangsun Han,Myong-In Lee,Jungho Im

doi:10.5194/amt-10-1859-2017

Abstract

Abstract. The detection of convective initiation (CI) is very important because convective clouds bring heavy rainfall and thunderstorms that typically cause severe socio-economic damage. In this study, deterministic and probabilistic CI detection models based on decision trees (DT), random forest (RF), and logistic regression (LR) were developed using Himawari-8 Advanced Himawari Imager (AHI) data obtained from June to August 2016 over the Korean Peninsula. A total of 12 interest fields that contain brightness temperature, spectral differences of the brightness temperatures, and their time trends were used to develop CI detection models. While, in our study, the interest field of 11.2 µm Tb was considered the most crucial for detecting CI in the deterministic models and the probabilistic RF model, the trispectral difference, i.e. (8.6–11.2 µm)–(11.2–12.4 µm), was determined to be the most important one in the LR model. The performance of the four models varied by CI case and validation data. Nonetheless, the DT model typically showed higher probability of detection (POD), while the RF model produced higher overall accuracy (OA) and critical success index (CSI) and lower false alarm rate (FAR) than the other models. The CI detection of the mean lead times by the four models were in the range of 20–40 min, which implies that convective clouds can be detected 30 min in advance, before precipitation intensity exceeds 35 dBZ over the Korean Peninsula in summer using the Himawari-8 AHI data.

Highlights

Atmospheric deep moist convection initiates shallow cumulus clouds, which may continue to grow vertically as cumulonimbus clouds, and this process is called convective initiation (CI; Banacos et al, 2005; Bluestein et al, 1990; Weckwerth and Parsons, 2006)
The objectives of this research were to (1) develop deterministic and probabilistic CI detection algorithms for Himawari-8 Advanced Himawari Imager (AHI) data based on rule-based decision trees and random forest approaches and a logistic regression modelling technique, (2) evaluate the CI detection models in terms of performance and efficiency, (3) assess the strengths and weaknesses of the deterministic and probabilistic CI detection models based on CI cases and validation data sets, and (4) examine key predictor variables for CI detection
The interest fields from Geostationary Operational Environmental Satellite (GOES)-R CI algorithm and threshold values have not been validated for Himawari-8 AHI

Summary

Introduction

Atmospheric deep moist convection initiates shallow cumulus clouds, which may continue to grow vertically as cumulonimbus clouds, and this process is called convective initiation (CI; Banacos et al, 2005; Bluestein et al, 1990; Weckwerth and Parsons, 2006). The decrease of atmospheric stability drives CI, which is attributed to various weather systems such as large-scale monsoonal fronts, the migration of frontal cyclones, and mesoscale convective systems (Craven et al, 2002; Houze, 2004; Mecikalski and Bedka, 2006). Such unstable weather systems can increase the potential risk of CI over a vast area, they trigger CI, occupying much smaller areas and making it difficult to predict the exact location. CI is characterized by the rapid variation of temperature and the increase of cloud tops, which can be effectively mea-

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