File Download

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

임정호

Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

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

Author(s)
Lee, SanggyunHan, HyangsunIm, JunghoJang, EunnaLee, Myong-In
Issued Date
2017-05
DOI
10.5194/amt-10-1859-2017
URI
https://scholarworks.unist.ac.kr/handle/201301/22178
Fulltext
http://www.atmos-meas-tech.net/10/1859/2017/
Citation
ATMOSPHERIC MEASUREMENT TECHNIQUES, v.10, no.5, pp.1559 - 1574
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 mu m T-b 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 mu m)-(11.2-12.4 mu m), 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.
Publisher
COPERNICUS GESELLSCHAFT MBH
ISSN
1867-1381
Keyword
CLOUD-TOP PROPERTIESHEAVY RAINFALLMETEOROLOGICAL IMAGERSTORM INITIATIONKOREAN PENINSULAPART IISATELLITECLASSIFICATIONREGRESSIONSYSTEMS

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.