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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Korean Meteorological Society
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Volume & Issues
Volume 16, Issue 4 - Dec 2006
Volume 16, Issue 3 - Sep 2006
Volume 16, Issue 2 - Jun 2006
Volume 16, Issue 1 - Mar 2006
Selecting the target year
Change in Statistical Characteristics of Typhoon Affecting the Korean Peninsula
Park, Jong-Kil ; Kim, Byung-Soo ; Jung, Woo-Sik ; Kim, Eun-Byul ; Lee, Dae-Gun ;
Atmosphere, volume 16, issue 1, 2006, Pages 1~17
The purpose of this study is to find out the change of statistical characteristics of typhoons affecting the Korean Peninsula. For this purpose, we analyzed the occurrence frequency of typhoon for 50 years (1954-2003) and change of air temperature and sea surface temperature near the Korean Peninsula in the same period. We classified typhoon tracks affecting the Korean Peninsula, and analyzed their trends and the amount of damage by typhoon. While the annual occurrence frequency of typhoon in the western North Pacific gradually decreased, its frequency affecting the Korean Peninsula increased. In addition, the occurrence location migrated northward. This coincides with the increase in air temperature and sea surface temperature around the Korean Peninsula. Typhoon tracks affecting the Korean Peninsula were classified into 7 types. Among them, the occurrence frequency of type 6 and 7 has increased. Although the occurrence frequency is low in type 2, the amount of damage by typhoon and occurrence frequency are increasing recently.
Study on the Sea Level Pressure Prediction of Typhoon Period in South Coast of the Korean Peninsula Using the Neural Networks
Park, Jong-Kil ; Kim, Byung-Soo ; Jung, Woo-Sik ; Seo, Jang-Won ; Shon, Yong-Hee ; Lee, Dae-Geun ; Kim, Eun-Byul ;
Atmosphere, volume 16, issue 1, 2006, Pages 19~31
The purpose of this study is to develop the statistical model to predict sea level pressure of typhoon period in south coast of the Korean Peninsula. Seven typhoons, which struck south coast of the Korean Peninsula, are selected for this study, and the data for analysis include the central pressure and location of typhoon, and sea level pressure and location of 19 observing site. Models employed in this study are the first order regression, the second order regression and the neural network. The dependent variable of each model is a 3-hr interval sea level pressure at each station. The cause variables are the central pressure of typhoon, distance between typhoon center and observing site, and sea level pressure of 3 hrs before, whereas the indicative variable reveals whether it is before or after typhoon passing. The data are classified into two groups - one is the full data obtained during typhoon period and the other is the data that sea level pressure is less than 1000 hPa. The stepwise selection method is used in the regression model while the node number is selected in the neural network by the Schwarz's Bayesian Criterion. The performance of each model is compared in terms of the root-mean square error. It turns out that the neural network shows better performance than other models, and the case using the full data produces similar or better results than the case using the other data.
Classification of Snowfalls over the Korean Peninsula Based on Developing Mechanism
Cheong, Seong-Hoon ; Byun, Kun-Young ; Lee, Tae-Young ;
Atmosphere, volume 16, issue 1, 2006, Pages 33~48
A classification of snowfall type based on development mechanism is proposed using previous snowfall studies, operational experiences, etc. Five types are proposed: snowfall caused by 1) airmass transformation (AT type), 2) terrain effects in a situation of expanding Siberian High (TE type), 3) precipitation systems associated with extratropical cyclones (EC type), 4) indirect effects of extratropical cyclones passing over the sea to the south of the Korean peninsula (ECS type), and 5) combined effects of TE and ECS types (COM type). Snowfall events during 1981-2001 are classified according to the 5 types mentioned above. For this, 118 events, with at least one station with daily snowfall depth greater than 20 cm, are selected. For the classification, synoptic weather charts, satellite images, and precipitation data are used. For TE and COM types, local sea-level pressure chart is also used to confirm the presence of condition for TE type (this is done for events in 1990 and thereafter). The classification shows that 109 out of 118 events can be classified as one of the 5 types. In the remaining 8 events, heavy snowfall occurred only in Ullung Island. Its occurrence may be due to one or more of the following mechanism: airmass transformation, mesoscale cyclones and/or mesoscale convergence over the East Sea, etc. Each type shows different characteristics in location of snowfall and composition of precipitation (i.e., dry snow, rain, and mixed precipitation). The AT-type snowfall occurs mostly in the west coast, Jeju and Ullung Islands whereas the TE-type snowfall occurs in the East coast especially over the Young Dong area. The ECS-type snowfall occurs mostly over the southern part of the peninsula and some east cost area (sometimes, whole south Korea depending on the location of cyclones). The EC- and COM-type snowfalls occur in wider area, often whole south Korea. Precipitation composition also varies with the type. The AT-type has a snow ratio (SR) higher than the mean value. The TE- and EC-type have SR similar to the mean. The ECS- and COM-type have SR values smaller than the mean. Generally the SR values at high latitude and mountainous areas are higher than those at the other areas. The SR value informs the characteristics of the precipitation composition. An SR value larger than 10 means that all precipitation is composed of snow whereas a zero SR value means that all precipitation is composed of rain.
Changes in the Diurnal Temperature Range due to Homecoming in the New Year Holiday Observed in Seoul for the 1954-2005 Period
Ho, Chang-Hoi ;
Atmosphere, volume 16, issue 1, 2006, Pages 49~53
The present study has examined interdecadal variations of the diurnal temperature range (DTR, daily maximum temperature minus daily minimum temperature) during the New Year season in Seoul for the period 1954-2005. Here, the average DTR for the New Year holidays (three consecutive days; one day before the New Year, the New Year day, and one day after the New Year) minus the average DTR for 14 days, 7 days before and 7 days after the New Year holidays, is defined for representing the New Year effect. The DTR index does not show notable trend until the late 1970s but shows obvious positive values afterward. For example, the difference of the average DTR between two periods (1980-2005 minus 1954-1979) is
, which is meaningful at the 95% confidence level. This result demonstrates that intense human activity even for the limited period may provide climate impact in local regions. Its plausible causes are discussed.