Identification of synoptic weather types over Taiwan area with multiple classifiers

CCUR > College of Natural Science and Engineering > College of Science > Department of Atmospheric Sciences & Graduate Institute of Earth Science / Atmospheric Science > journal articles > Item 987654321/41809

Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/41809

Title:	Identification of synoptic weather types over Taiwan area with multiple classifiers
Authors:	Su, SH (Su, Shih-Hao) Chu, JL (Chu, Jung-Lien) Yo, TS (Yo, Ting-Shuo) Lin, LY (Lin, Lee-Yaw)
Contributors:	大氣科學系
Keywords:	front machine learning synoptic weather classification heavy rainfall Taiwan CLASSIFICATION CLIMATE typhoon KeyWords Plus
Date:	2018-12
Issue Date:	2019-01-16 11:11:16 (UTC+8)
Abstract:	In this study, a novel machine learning approach was used to classify three types of synoptic weather events in Taiwan area from 2001 to 2010. We used reanalysis data with three machine learning algorithms to recognize weather systems and evaluated their performance. Overall, the classifiers successfully identified 52-83% of weather events (hit rate), which is higher than the performance of traditional objective methods. The results showed that the machine learning approach gave low false alarm rate in general, while the support vector machine (SVM) with more principal components of reanalysis data had higher hit rate on all tested weather events. The sensitivity tests of grid data resolution indicated that the differences between the high- and low-resolution datasets are limited, which implied that the proposed method can achieve reasonable performance in weather forecasting with minimal resources. By identifying daily weather systems in historical reanalysis data, this method can be used to study long-term weather changes, to monitor climatological-scale variations, and to provide better estimate of climate projections. Furthermore, this method can also serve as an alternative of model output statistics and potentially be used for synoptic weather forecasting.
Appears in Collections:	[Department of Atmospheric Sciences & Graduate Institute of Earth Science / Atmospheric Science ] journal articles

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	325	View/Open

View Licence

Loading...