Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Monthly temperature forecasting using large-scale climate teleconnections and multiple regression models

대규모 기후 원격상관성 및 다중회귀모형을 이용한 월 평균기온 예측

  • Kim, Chul-Gyum (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Jeongwoo (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Lee, Jeong Eun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Nam Won (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Kim, Hyeonjun (Department of Hydro Science and Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 김철겸 (한국건설기술연구원 수자원하천연구본부) ;
  • 이정우 (한국건설기술연구원 수자원하천연구본부) ;
  • 이정은 (한국건설기술연구원 수자원하천연구본부) ;
  • 김남원 (한국건설기술연구원 수자원하천연구본부) ;
  • 김현준 (한국건설기술연구원 수자원하천연구본부)
  • Received : 2021.07.01
  • Accepted : 2021.07.22
  • Published : 2021.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, the monthly temperature of the Han River basin was predicted by statistical multiple regression models that use global climate indices and weather data of the target region as predictors. The optimal predictors were selected through teleconnection analysis between the monthly temperature and the preceding patterns of each climate index, and forecast models capable of predicting up to 12 months in advance were constructed by combining the selected predictors and cross-validating the past period. Fore each target month, 1000 optimized models were derived and forecast ranges were presented. As a result of analyzing the predictability of monthly temperature from January 1992 to December 2020, PBIAS was -1.4 to -0.7%, RSR was 0.15 to 0.16, NSE was 0.98, and r was 0.99, indicating a high goodness-of-fit. The probability of each monthly observation being included in the forecast range was about 64.4% on average, and by month, the predictability was relatively high in September, December, February, and January, and low in April, August, and March. The predicted range and median were in good agreement with the observations, except for some periods when temperature was dramatically lower or higher than in normal years. The quantitative temperature forecast information derived from this study will be useful not only for forecasting changes in temperature in the future period (1 to 12 months in advance), but also in predicting changes in the hydro-ecological environment, including evapotranspiration highly correlated with temperature.

본 연구에서는 글로벌 기후지수 및 한강권역의 기상자료를 예측인자로 하는 통계적 다중회귀모형을 구성하여 한강권역의 월 평균기온에 대한 장기예측을 수행하였다. 예측대상인 월 평균기온과 선행기간별 예측인자와의 원격상관성 분석을 기반으로 최적의 예측인자를 선별하였으며, 선별된 예측인자를 조합하고 과거기간에 대한 교차검증을 통하여 최대 12개월까지 선행예측이 가능한 다중회귀모형 기반의 예측모형을 구성하였다. 과거 1992년 1월부터 2020년 12월을 대상으로 월 평균기온에 대한 예측성을 분석한 결과, PBIAS는 -1.4 ~ -0.7%, RSR은 0.15 ~ 0.16, NSE는 0.98, r은 0.99로 높은 적합성을 나타내었다. 각 월별 관측치가 예측범위에 포함될 확률은 평균 약 64.4%로 나타났으며, 월별로는 9월, 12월, 2월, 1월의 예측성이 상대적으로 높고, 4월, 8월, 3월의 예측성이 낮은 것으로 분석되었다. 평년 대비 유난히 낮거나 높은 기온을 보였던 일부 기간을 제외하고 대체로 예측치의 범위 또는 예측치의 중앙값 등이 관측치와 잘 부합하는 것으로 나타났다. 본 연구에서 도출되는 정량적 기온예측정보는 미래기간(선행 1 ~ 12개월)의 기온에 대한 변화뿐만 아니라, 기온과 상관도가 높은 증발산량을 비롯한 수문생태환경에 대한 변화를 전망하는 경우에도 유용하게 활용될 수 있을 것이다.

Keywords

Acknowledgement

본 연구는 한국건설기술연구원 주요사업 "가뭄대응 중소 하천 물부족 위험도 평가 및 물 확보 기술 개발" 과제의 연구비 지원에 의해 수행되었습니다.

References

  1. Barnston, A.G., and Livezey, R.E. (1987). "Classification, seasonality and persistence of low-frequency atmospheric circulation patterns." Monthly Weather Review, Vol. 115, No. 6, pp. 1083-1126. https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2
  2. Doblas-Reyes, F.J., Hagedorn, R., and Palmer, T.N. (2005). "The rationale behind the success of multi-model ensembles in seasonal forecasting - II. Calibration and combination." Tellus A: Dynamic Meteorology and Oceanography, Vol. 57, No. 3, pp. 234-252. https://doi.org/10.1111/j.1600-0870.2005.00104.x
  3. He, S., Gao, Y., Li, F., Wang, H., and He, Y. (2017). "Impact of Arctic oscillation on the East Asian climate: A review." Earth-Science Reviews, Vol. 164, pp. 48-62. https://doi.org/10.1016/j.earscirev.2016.10.014
  4. Jeong, J.-H., and Ho, C.-H. (2005). "Changes in occurrence of cold surges over east Asia in association with Arctic Oscillation." Geophysical Research Letters, Vol. 32, L14704. doi:10.1029/2005GL023024
  5. Kim, C.-G., Lee, J., Lee, J.E., Kim, N.W., and Kim, H. (2020). "Monthly precipitation forecasting in the Han River basin, South Korea, using large-scale teleconnections and multiple regression models." Water, Vol. 12, No. 6, 1590. doi: 10.3390/w12061590
  6. Kim, H.-J., Oh, S.M., and Chung, I.-U. (2018). "An empirical model approach for seasonal prediction of summer temperature in South Korea." Journal of Climate Research, Vol. 13, No. 3, pp. 17-35. https://doi.org/10.14383/cri.2018.13.1.17
  7. Kim, S.T., Soh, S.-J., and Kug, J.-S. (2017). "Winter temperatures over the Korean Peninsula and East Asia: Development of a new index and its application to seasonal forecast." Climate Dynamics, Vol. 49, pp. 1567-1581. https://doi.org/10.1007/s00382-016-3402-2
  8. Lee, J.H., Julien, P.Y., and Maloney, E.D. (2019). "The variability of South Korean temperature associated with climate indicators." Theoretical and Applied Climatology, Vol. 138, pp. 469-489. https://doi.org/10.1007/s00704-019-02842-8
  9. Li, F., Wang. H., and Gao, Y. (2014). "On the strengthened relationship between the East Asian winter monsoon and Arctic Oscillation: A comparison of 1950-70 and 1983-2012." Journal of Climate, Vol. 27, No. 13, pp. 5075-5091. https://doi.org/10.1175/JCLI-D-13-00335.1
  10. Li, J., and Zeng, Q. (2002). "A unified monsoon index." Geophysical Research Letters, Vol. 29, No. 8, pp. 115-1-115-4.
  11. Lim, Y.-K., and Kim, H.-D. (2013). "Impact of the dominant largescale teleconnections on winter temperature variability over East Asia." Journal of Geophysical Research: Atmospheres, Vol. 118, No. 14, pp. 7835-7848. https://doi.org/10.1002/jgrd.50462
  12. Park, H.-J., and Ahn, J.-B. (2016). "Combined effect of the Arctic oscillation and the Western Pacific pattern on East Asia winter temperature." Climate Dynamics, Vol. 46, pp. 3205-3221. https://doi.org/10.1007/s00382-015-2763-2
  13. Schepen, A., Wang, Q.J., and Robertson, D.E. (2012). "Combining the strengths of statistical and dynamical modeling approaches for forecasting Australian seasonal rainfall." Journal of Geophysical Research, Vol. 117, D20107. doi: 10.1029/2012JD018011
  14. Woo, S.-H., Choi, J., and Jeong, J.-H. (2020). "Modulation of ENSO teleconnection on the relationship between Arctic oscillation and wintertime temperature variation in South Korea." Atmosphere, Vol. 11, 950. doi: 10.3390/atmos11090950
  15. Yoo, C., Johnson, N.C., Chang, C.-H., Feldstein, S.B., and Kim, Y.-H. (2018). "Subseasonal prediction of wintertime East Asia temperature based on atmospheric teleconnections." Journal of Climate, Vol. 31, No. 22, pp. 9351-9366. https://doi.org/10.1175/jcli-d-17-0811.1