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1 The Spatial and temporal distributions of NET(Net Effective Temperature) with a Function of Temperature, Humidity and Wind Speed in Korea* Inhye Heo**, Youngeun Choi***, and Won-Tae Kwon**** Abstract : This paper examined the possibility of NET application for a relative weather stress index in Korea. The characteristic of NET distribution used temperature, relative humidity, wind speed which forecasting at Korean Meteorological Administration were analyzed. Regional critical values of daily maximum NET of stress index for summer resembled the distribution of daily maximum temperature because were not impacted wind and humidity but temperature. Regional critical values of daily minimum NET of stress index for winter distributed variously compared with summer. The highland region and the northern region of Seoul were impacted of low temperature and coastal region which strong wind. The occurrences of stressful days did not vary in summer, but obviously increased in winter after mid-199s. Key Words : weather stress index, net effective temperature, daily maximum temperature, relative humidity, strong wind (Graduate Student, Department of Geography, Konkuk University) (Assistant Professor, Department of Geography, Konkuk University). (Director, Climate Research Laboratory, Meteorological Research Institute, Korea Meteorological Administration). 13

2 Fig. 1. The location of weather stations used for the study 14

3 Fig. 2. Change of NET as a function of wind speed and temperature for dry conditions (RH=3%) and wet conditions (RH=9%) 15

4 = Fig. 3. Monthly distribution of absolute daily maximum NET and daily minimum NET in Seoul (1991 ) Fig. 4. The same as Fig. 3 except for Jeju 16

5 Table 1. Difference of temperature, wind speed and relative humidity between January and February in Seoul and Jeju Temperature( C) Wind speed(m/s) Relative humidity(%) Time Seoul Jeju Time Jan Feb Diff. Jan Feb Diff

6 days 25 Seoul Jeju month Fig. 5. Monthly distribution of the number of days with NET 99% in warm season (1991~) days 3 25 Seoul Jeju month Fig. 6. Monthly distribution of the number of days with NET 1% in cold season (1991~) 18

7 4 Wind chill index - Heat index Daily Min NET Daily Max NET Fig. 7. Correlation of NET with wind-chill index and heat index in Seoul (Left: winter daily minimum NET and wind-chill index, Right: summer daily maximum NET and heat index) 3 4 Wind chill index Heat index Daily Min NET Daily Max NET Fig. 8. The same as Fig. 7 except for Jeju 19

8 days Seoul Jeju Hour of day Fig. 9. Diurnal variations of the occurrences of extreme WSI 97.5% in summer (1991~)

9 days Seoul Jeju Hour of day Fig.. The same as Fig 9. except for WSI 2.5% in winter (1991~) Fig. 11. Spatial Distribution of daily maximum NET critical value in summer (1991~) 21

10 Table 2. Difference of temperature, wind speed and humidity of very stressful days in summer at two adjacent cities. ( C) Mean wind speed(m/s) Mean humidity(%) Gangneung Sokcho Difference Busan Ulsan Difference Fig. 12. Spatial distribution of daily maximum temperature in summer (1991~) Fig. 13. Spatial distribution of daily minimum NET critical value in winter(1991~) 22

11 Table 3. Difference of regional temperature, wind speed and humidity of strongly stressful days in winter at two adjacent cities ( C) Mean Temp.( C) Mean wind speed(m/s) Mean humidity(%) Wando Tongyeong Difference Wuljin Pohang Difference days 5 summer winter sum year Fig. 14. Annual variations of the occurrences of extreme WSI for WSI 2.5% in winter and WSI?97.5% in summer 23

12 days winter 3 year moving average year Fig. 15. Trend of the occurrences of extreme WSI 2.5% in winter Table 4. Comparison normal mean temperature with strongly stressful year mean temperature in summer (unit: C) June July August Normal (1971~) Difference

13 Diaz, J., Garcia, R., Velazquez ds Castro, F., Hernandez, E., Lopez, C., and Otero, A., 2, Effective of extremely hot days on people older than 65 years in Seville(Spain) from 1986 to 1997, International Journal of Biometeorology, 46, Hentschel, L. S., 1987, A human biometeorology classification of climate for large and local scales, In Proc. WMO/HMO/UNEP Symposium on Climate and Human Health, Leningrad 1986,, WCPA-No. 1, WMO. Kalkstein, L. S. and Valimont, K. M., 1986, An evaluation of summer discomfort in the United States using a relative climatological index, Bull. Am. Meteorol. Soc., 67(7), Kalkstein, L. S. and Valimont, K. M., 1987, An evaluation of winter weather severity in the United States using the weather stress index, Bull. Am. Meteorol. Soc., 68(12), Kalkstein, L. S., Jamoson, P. F., Greene, J. S., Libby J., and Robinson, L., 1996, The Philadelphia Hot Weather-Health Watch/Warning System: development and application, Summer 1995, Bull. Am. Meteorol. Soc., 77(7), Li, P. W. and Chan, S. T.,, Application of a weather stress index for alerting the public to stressful weather in Hong Kong, Meteorological Applications, 7, Matzarakis, A. and Mayer, H., 1997, Heat stress in 25

14 Greece, International Journal of Biometeorology, 41(1), Nkiolopoulou, M., 1, The effect of climate on the use of open spaces in the urban environment, International Society of Biometeorology, Siple, P. A. and Passel, C. F., 1945, Measurements of dry atmospheric coolong in subfreezing temperatures, Proc. Amer. Phil. Soc., 89, ( ). Smoyer, K. E., 1998, A comparative analysis of heat waves and associated mortality in St. Louis Missouri-198 and 1995, International Journal of Biometeorology, 42(1), Steadman, R. G., 1971, Index of windchill of clothed persons, Journal of applied meteorology,, Steadman, R. G., 1984, A universal scale of apparent temperature, Journal of applied meteorology, 23, WMO/HMO/UNEP, 1996, Climate Change and Human Health, WHO

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