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Bayesian change-point analysis is applied to detect a change-point in the occurrences of tropical night (TN) days in the 50-year time series data for five major cities in Republic of Korea. A TN day is simply defined as a day when the daily minimum temperature is greater than 25∘C. A Bayesian analysis is performed for detecting a change-point at an unknown time point in the TN day frequency time series, which ismodeled by an independent Poisson randomvariable.The results showed that a single change occurred around 1993 for three cities (Seoul, Incheon, andDaegu).However,whenwe excluded the extraordinary year, 1994, a single change occurred around 1993 only in Seoul and Daegu. The average number of TN days in Seoul and Daegu increased significantly, by more than 150%, after the change-point year. The abrupt increase in TN day frequency in two cities over Republic of Korea around 1993 may be related to the significant decadal change in the East Asian summer monsoon around the mid 1990s and to rapid urbanization.

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Cho, A-Ra, Y.-Y. Choi and M.-S. Suh, 2015: Improvements of a COMS land surface temperature retrieval algorithm based on the temperature lapse rate and water vapor/aerosol effect, Remote sens., 7, 1777-1797.  
Suh, M.-S. and C. Kim, 2015: Change-Point Analysis of Tropical Night Occurrences for Five Major Cities in Republic of Korea. Advances in Meteorology, 2015, 1-11, doi:10.1155/2015/801981.  
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Suh, M.-S et al, Projections of High Resolution Climate Changes for South Korea Using Multiple-Regional Climate Models Based on Four RCP Scenarios. Part 1: Surface Air Temperature. APJAS, 52(2), 151-169  
Oh, S.-G et al. Projection of high resolution climate change for South Korea using multiple-regional climate models based on four RCP scenarios. Part 2: Precipitation. APJAS, 52(2), 171-189  

Meteorological Satellite Research Lab.
Department of Atmospheric Science Konju National University
56, Gongjudaehak-ro, Gongju-si, Chungcheongnam-do, Korea
TEL : +82-41-850-8533 l E-mail : sms4@kongju.ac.kr

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