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인지온도 확률예보기반 폭염-건강영향예보 지원시스템 개발 및 2019년 온열질환자를 이용한 평가

Development of Impact-based Heat Health Warning System Based on Ensemble Forecasts of Perceived Temperature and its Evaluation using Heat-Related Patients in 2019

김규랑|강미선|Miloslav Belorid
한국기상학회 (Korean Meteorological Society)|2020.06|대기 (Atmosphere)|제30권 제2호|pp. 195-207|DOI 10.14191/Atmos.2020.30.2.195

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참고문헌

키워드

risk matrixLENSPerceived temperatureheat-related riskimpact-based forecasts
주제분류대기과학

초록

This study aims to introduce the structure of the impact-based heat health warning system on 165 counties in South Korea developed by the National Institute of Meteorological Sciences. This system was developed using the daily maximum perceived temperature (PTmax), which is a human physiology-based thermal comfort index, and the Local ENSemble prediction system for the probability forecasts. Also, A risk matrix proposed by the World Meteorological Organization was employed for the impact-based forecasts of this system. The threshold value of the risk matrix was separately set depending on regions. In this system, the risk level was issued as four levels (GREEN, YELLOW, ORANGE, RED) for first, second, and third forecast lead-day (LD1, LD2, and LD3). The daily risk level issued by the system was evaluated using emergency heat-related patients obtained at six cities, including Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan, for LD1 to LD3. The high-risks level occurred more consistently in the shorter lead time (LD3→LD1) and the performance (rs) was increased from 0.42 (LD3) to 0.45 (LD1) in all cities. Especially, it showed good performance (rs = 0.51) in July and August, when heat stress is highest in South Korea. From an impact-based forecasting perspective, PTmax is one of the most suitable temperature indicators for issuing the health risk warnings by heat in South Korea.

Abstract

This study aims to introduce the structure of the impact-based heat health warning system on 165 counties in South Korea developed by the National Institute of Meteorological Sciences. This system was developed using the daily maximum perceived temperature (PTmax), which is a human physiology-based thermal comfort index, and the Local ENSemble prediction system for the probability forecasts. Also, A risk matrix proposed by the World Meteorological Organization was employed for the impact-based forecasts of this system. The threshold value of the risk matrix was separately set depending on regions. In this system, the risk level was issued as four levels (GREEN, YELLOW, ORANGE, RED) for first, second, and third forecast lead-day (LD1, LD2, and LD3). The daily risk level issued by the system was evaluated using emergency heat-related patients obtained at six cities, including Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan, for LD1 to LD3. The high-risks level occurred more consistently in the shorter lead time (LD3→LD1) and the performance (rs) was increased from 0.42 (LD3) to 0.45 (LD1) in all cities. Especially, it showed good performance (rs = 0.51) in July and August, when heat stress is highest in South Korea. From an impact-based forecasting perspective, PTmax is one of the most suitable temperature indicators for issuing the health risk warnings by heat in South Korea.

참고문헌 (53)

  1. ASHRAE, "ASHRAE Handbook Fundamentals-8", American Society of Heating Refrigerating and Air-Conditioning Engineers Inc, 892~ (2001)
  2. Azhar, G. S., "Heat-related mortality in India: excess all-cause mortality associated with the 2010 Ahmedabad heat wave", PLoS One, 9, pp.e91831~ (2010)[DOI]
  3. Miloslav Belorid, "Bias Correction of Short-Range Ensemble Forecasts of Daily Maximum Temperature Using Decaying Average", Asia-Pacific Journal of Atmospheric Sciences, 56(4), pp.503~514 (2020), 한국기상학회[DOI]
  4. "MENEX_2005-the updated version of man-environment heat exchange model" (2005)[URL]
  5. Błażejczyk, A., "Heat stress mortality and desired adaptation responses of healthcare system in Poland", Int. J. Biometeorol, 62, pp.307~318 (2018)[DOI]
  6. 변재영, "한반도 2007년 여름철 인지온도 특성 연구", 대기, 18(2), pp.137~146 (2008), 한국기상학회
  7. 변재영, "중규모 수치 모델 자료를 이용한 2007년 여름철 한반도 인지온도 예보와 검증", 대기, 18(3), pp.237~248 (2008), 한국기상학회
  8. "Warning Signal-Heat wave" (2019)[URL]
  9. D’Souza, A. W., "Intermittent sequential pneumatic compression does not enhance whole-body heat loss in elderly adults during extreme heat exposure", Appl. Physiol. Nutr. Metab, 44, pp.1383~1386 (2019)[DOI]
  10. Fanger, P. O., "Thermal comfort-Analysis and Applications in Environmental Engineering", pp.244~ (1970), Danish Technical Press
  11. Gagge, A. P., "A practical system of units for the description of the heat exchange of man with his environment", Science, 94, pp.428~430 (1941)
  12. Gagge, A. P., "A standard predictive index of human response to the thermal environment", ASHRAE Trans., 92, pp.709~731 (1986)
  13. Giannaros, T. M., "Operational forecasting of humanbiometeorological conditions", Int. J. Biometeorol., 62, pp.1339~1343 (2018)[DOI]
  14. Gosling, S. N., "Associations between elevated atmospheric temperature and human mortality:a critical review of the literature", Clim. Change, 92, pp.299~341 (2009)
  15. Gosling, S. N., "A glossary for biometeorology", Int. J. Biometeorol, 58, pp.277~308 (2014)[DOI]
  16. Guest, C. S., "Climate and mortality in Australia: retrospective study, 1979-1990, and predicted impacts in five major cities in 2030", Clim. Res, 13, pp.1~15 (1999)
  17. Hajat, S., "Impact of hot temperatures on death in London:a time series approach", J. Epidemiol. Community Health, 56, pp.367~372 (2002)
  18. Hajat, S., "Mortality displacement of heat-related deaths-A comparison of Delhi, São Paulo, and London", Epidemiology, 16, pp.613~620 (2005)
  19. Heaviside, C., "Heat-related mortality in Cyprus for current and future climate scenarios", Sci. Total Environ, 569, pp.627~633 (2016)[DOI]
  20. Höppe, P., "The physiological equivalent temperature-a universal index for the biometeorological assessment of the thermal environment", Int. J. Biometeorol, 43, pp.71~75 (1999)[DOI]
  21. Huynen, M. M., "The impact of heat waves and cold spells on mortality rates in the Dutch population", Environ. Health Perspect, 109, pp.463~470 (2001)
  22. 황미경, "하계 항만열환경정보 제공을 위한 열환경 평가 및 예보시스템 구축", 한국환경보건학회지, 42(2), pp.92~101 (2016), 한국환경보건학회
  23. 황미경, "보건기상정보 제공을 위한 폭염특보 운영현황 및 개선방안 조사", 한국환경과학회지, 28(2), pp.277~285 (2019), 한국환경과학회[DOI]
  24. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, "IPCC", Cambridge University Press, 1535~ (2013)
  25. "Weather Warning/Advisories" (2019)[URL]
  26. Jendritzky, G., "The perceived temperature: The method of the Deutscher Wetterdienst for the assessment of cold stress and heat load for the human body", Proc. Internet Workshop on Windchill (2000), Meteorological Service of Canada
  27. Jendritzky, G., "UTCI-Why another thermal index?", Int. J. Biometeorol, 56, pp.421~428 (2012)[DOI]
  28. Kang, M., "Event-based heat-related risk assessment model for South Korea using maximum perceived temperature, wet-bulb globe temberature, and air temperature data", Int. J. Environ. Res. Public Health, 17, pp.2631~ (2020)[DOI]
  30. 김지영, "한국 주요도시의 폭염에 대한 기후 순응도 특성", 대기, 19(4), pp.309~318 (2009), 한국기상학회
  31. 김도우, "우리나라 폭염 인명피해 발생특징", 대기, 24(2), pp.225~234 (2014), 한국기상학회
  32. 김영준, "2013년 부산지역 폭염사례일의 열쾌적성 평가", 한국환경과학회지, 23(11), pp.1929~1941 (2014), 한국환경과학회[DOI]
  33. Kim, K. R., "A test-bed for impact-based forecasts on human health during heat waves by implementing the threshold temperature and probability forecast", Proc. Autumn Meeting of KMS, pp.193~ (2018)
  34. Meteorological Technology & Policy vol. 9No. 1, "KMA", Korea Meteorological Administration, 74~ (2016)
  35. "Korea Meteorological Administration" (2019)[URL]
  36. Koppe, C., "The heat health warning system of the German meteorological service. In UmweltMedizinischer InformationsDienst", UMID Special Issue -Climate Change and Health, 39~43 (2009)
  37. Kovats, R. S., "Heat stress and public health: a critical review", Annu. Rev. Public Health, 29, pp.41~55 (2008)
  38. Kysely, J., "Mortality during heat waves in South Korea, 1991 to 2005: How exceptional was the 1994 heat wave?", Clim. Res, 38, pp.105~116 (2009)
  39. Matzarakis, A., "The heat health warning system in Germany-Application and warnings for 2005 to 2019", Atmosphere, 11, pp.170~ (2020)[DOI]
  40. Heat waves and health: Guidance on warning system development, "McGregor, G. R.", World Meteorological Organization and World Health Organization, 96~ (2015)
  41. "UK weather warnings" (2018)[URL]
  43. "Heat Stress Index: WBGT" (2019)[URL]
  44. National weather service reference guide, "NOAA", National Oceanic and Atmospheric Administration, 133~ (2010)
  45. Advanced Research on Biometeorology and Urban Meteorology development Research of Advanced Applied Meteorological Instrument, "NIMS", National Institute of Meteorological Sciences, 107~ (2018)
  46. Parsons, K., "Human Thermal Environments: The Effects of Hot, Moderate, and Cold Environments on Human Health, Comfort, and Performance", pp.635~ (2002), CRC Press, Inc
  47. Saez, M., "Ischaemic heart disease mortality and weather temperature in Barcelona, Spain", Eur. J. Public Health, 10, pp.58~63 (2000)
  48. Sheridan, S. C., "Progress in heat watch-Warning system technology", Bull. Amer. Meteor. Soc, 85, pp.1931~1942 (2004)
  49. Staiger, H., "The perceived temperature-a versatile index for the assessment of the human thermal environment. Part A:scientific basics", Int. J. Biometeorol., 56, pp.165~176 (2012)[DOI]
  50. Thorsson, S., "Different methods for estimating the mean radiant temperature in an outdoor urban setting", Int. J. Climatol, 27, pp.1983~1993 (2007)
  51. Urban, A., "Comparison of UTCI with other thermal indices in the assessment of heat and cold effects on cardiovascular mortality in the Czech Republic", Int. J. Environ. Res. Public Health, 11, pp.952~967 (2014)[DOI]
  52. Wilks, D. S., "Statistical Methods in the Atmospheric Sciences", pp.648~ (2006), Academic Press
  53. WMO Guideline on multi-hazard impactbased forecast and warning services, "WMO", World Meteorological Organization, 23~ (2015)

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