DOI QR코드

DOI QR Code

Assessment of predictability of categorical probabilistic long-term forecasts and its quantification for efficient water resources management

효율적인 수자원관리를 위한 범주형 확률장기예보의 예측력 평가 및 정량화

  • Received : 2017.05.23
  • Accepted : 2017.06.30
  • Published : 2017.08.31

Abstract

As the uncertainty of precipitation increases due to climate change, seasonal forecasting and the use of weather forecasts become essential for efficient water resources management. In this study, the categorical probabilistic long-term forecasts implemented by KMA (Korea Meteorological Administration) since June 2014 was evaluated using assessment indicators of Hit Rate, Reliability Diagram, and Relative Operating Curve (ROC) and a technique for obtaining quantitative precipitation estimates based on probabilistic forecasts was proposed. The probabilistic long-term forecasts showed its maximum predictability of 48% and the quantified precipitation estimates were closely matched with actual observations; maximum correlation coefficient (R) in predictability evaluation for 100% accurate and actual weather forecasts were 0.98 and 0.71, respectively. A precipitation quantification approach utilizing probabilistic forecasts proposed in this study is expected to enable water management considering the uncertainty of precipitation. This method is also expected to be a useful tool for supporting decision-making in the long-term planning for water resources management and reservoir operations.

기후변화로 인해 강수의 불확실성이 증가하는 현 시점에서 효율적인 물 관리를 위한 계절예측 및 기상 예보의 활용은 필수적이다. 본 연구에서는 기상청에서 2014년 6월부터 시행하고 있는 범주형 확률장기예보를 Hit Rate, Reliability Diagram, Relative Operating Curve (ROC)의 평가지표를 활용하여 예측력을 검증하였고, 추가적으로 확률예보를 활용하여 정량적인 예측 강수량을 생산하는 기법을 제안하였다. 확률장기예보의 예측력 검증결과 최대 48%의 예측력을 갖는 것을 확인할 수 있었다. 확률예보를 활용하여 예측 강수량을 추정한 결과, 정량적으로 관측 자료와 유사하게 모의되는 것을 확인할 수 있었으며 예측 적합도 평가결과 100%의 정확도를 가진 예보의 경우 최대 0.98, 실제 예보의 경우 최대 0.71의 상관계수를 보였다. 본 연구에서 제안하는 확률예보를 활용한 예측 강수량 추출기법은 강수의 불확실성을 고려한 물 관리를 가능하게 해줄 것으로 판단되며 효율적인 수자원 장기 이수계획 및 저수지 운영의 의사결정지원 등에 활용 가능할 것으로 기대된다.

Keywords

References

  1. Croley II, T. E. (2000). Using meteorology probability forecasts in operational hydrology. ASCE Press, p. 206.
  2. Gouweleeuw, B. T., Thielen, J., Franchello, G., De Roo, A. P. J., and Buizza, R. (2005). "Flood forecasting using medium-range probabilistic weather prediction." Hydrology and Earth System Sciences, Vol. 9, No. 4, pp. 309-368.
  3. Kang, B., Rleu, S. Y., and Ko, I. H. (2005). "Long-term streamflow prediction for integrated real-time water management system.", Proceedings of the Korea Water Resources Association Conference 2005, KWRA
  4. Kang, J. W. (2013). "Probabilistic forecasting of seasonal inflow to reservoir." Journal of Environmental Science International, Vol. 22, No. 8, pp. 965-977. https://doi.org/10.5322/JESI.2013.22.8.965
  5. Kim, J. C., Kim, J., and Lee, S. J. (2011). "Improvement of mid/long-term ESP scheme using probabilistic weather forecasting." Journal of Korea Water Resources Association, Vol. 44, No. 10, pp. 843-851. https://doi.org/10.3741/JKWRA.2011.44.10.843
  6. KMA (Korea Meteorological Administration) (2014). The improvement of regional long-term forecast system. KMA Publication No. 11-136000-001095-14, KMA, pp. 65-73.
  7. KMA (Korea Meteorological Administration) (2016). A forecast characteristics analysis technical note of GloSea5. KMA, pp. 3-14.
  8. Persson, A., Andersson, E., and Tsonevsky, I. (2015). User guide to ECMWF forecast products. Ver.1.2, ECWMF, pp. 101-113.
  9. Simpson, H. J., Cane, M. A., Herczeg, A. L., Zebiak, S. E., and Simpson, J. H. (1993). "Annual river discharge in Southeastern Australia related to El Nino-southern oscillation forecasts of sea surface temperatures." Water Resources Research, Vol. 34, No. 11, pp. 3035-3044. https://doi.org/10.1029/98WR02406
  10. Stedinger, J. R., and Kim, Y. O. (2010). "Probabilities for ensemble forecast reflecting climate information." Journal of Hydrology, Vol. 391, pp. 9-23. https://doi.org/10.1016/j.jhydrol.2010.06.038
  11. Yu, P. S., Yang, T. C., Kuo, C. M., and Wang, Y. T. (2014). "A stochastic approach for seasonal water-shortage probability forecasting based on seasonal weather outlook." Water Resources Management, Vol. 28, No. 12, pp. 3905-3920. https://doi.org/10.1007/s11269-014-0717-9