Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches

Abstract

As convective clouds in Northeast Asia are accompanied by various hazards related with heavy rainfall and thunderstorms, it is very important to detect convective initiation (CI) in the region in order to mitigate damage by such hazards. In this study, a novel approach for CI detection using images from Meteorological Imager (MI), a payload of the Communication, Ocean, and Meteorological Satellite (COMS), was developed by improving the criteria of the interest fields of Rapidly Developing Cumulus Areas (RDCA) derivation algorithm, an official CI detection algorithm for Multi-functional Transport SATellite-2 (MTSAT-2), based on three machine learning approaches-decision trees (DT), random forest (RF), and support vector machines (SVM). CI was defined as clouds within a 16 × 16 km window with the first detection of lightning occurrence at the center. A total of nine interest fields derived from visible, water vapor, and two thermal infrared images of MI obtained 15-75 min before the lightning occurrence were used as input variables for CI OPEN ACCESSRemote Sens. 2015, 7 9185 detection. RF produced slightly higher performance (probability of detection (POD) of 75.5% and false alarm rate (FAR) of 46.2%) than DT (POD of 70.7% and FAR of 46.6%) for detection of CI caused by migrating frontal cyclones and unstable atmosphere. SVM resulted in relatively poor performance with very high FAR ~83.3%. The averaged lead times of CI detection based on the DT and RF models were 36.8 and 37.7 min, respectively. This implies that CI over Northeast Asia can be forecasted ~30-45 min in advance using COMS MI data.

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

“…The objectives of this research were to (1) develop deterministic and probabilistic CI detection algorithms for Himawari-8 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. This study extends our previous research in Han et al (2015), where the COMS MI data were used. One of the main limitations of using COMS MI data in the previous study is its relatively coarse spatial resolution (4 km), which is not enough to detect small convective clouds.…”

“…Therefore, these geostationary satellites can be extremely useful in CI nowcasting. Previous studies developed CI nowcasting algorithms for geostationary satellites by determining a threshold or a range of values of T b at specific channels, and their spectral and/or temporal differences (Mecikalski and Bedka, 2006;Mecikalski et al, 2008;Walker et al, 2012;Morel and Senesi, 2002;Jewett and Mecikalski, 2013;Merk and Zinner, 2013;Siewert et al, 2010;Sobajima, 2012;Han et al, 2015). Geostationary Operational Environmental Satellite (GOES) systems and Meteorological Second Generation (MSG) are the representative geostationary satellites operated at the National Oceanic and Atmospheric Administration (NOAA) and European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), respectively.…”

“…The performance of the MTSAT-2 RDCA algorithm needs to be evaluated in different times and circumstances. Han et al (2015) developed CI detection algorithms for COMS meteorological imager (MI) data by determining new rules and thresholds for the interest fields used in the RDCAs through machine learning, based on the fact that the characteristics of the spectral channels of COMS MI are similar to those of MTSAT-2. The algorithms were validated for various CI cases over Korea, demonstrating good performance with a POD as high as 75.5 % and a FAR as low as 46.2 %.…”

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

“…Machine learning is a novel approach used in various remote sensing applications, including land cover/land use classification [46][47][48][49][50][51][52], change detection [53,54], geological mapping [55], vegetation mapping [56][57][58][59], hydrological studies [60][61][62] and atmospheric studies [63,64]. In this study, two rule-based machine learning approaches-decision tree (DT) and random forest (RF)-were used for the classification of open water, sea ice and melt pond from the TerraSAR-X dual-polarization [65], was used to carry out the DT-based classification.…”

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

“…In addition, random forest provides the relative importance of a variable using out-of-bag data when the variable is permuted. Because of these strengths, random forest has proven robust in various remote sensing applications [61][62][63][64][65][66][67][68].…”