Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
권호 표지
DOI QR코드

DOI QR Code

A Correction of East Asian Summer Precipitation Simulated by PNU/CME CGCM Using Multiple Linear Regression

다중 선형 회귀를 이용한 PNU/CME CGCM의 동아시아 여름철 강수예측 보정 연구

  • Hwang, Yoon-Jeong (Department of Atmosphere Science, Pusan National University) ;
  • Ahn, Joong-Bae (Department of Atmosphere Science, Pusan National University)
  • Published : 2007.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.

강수는 다양한 대기 변수들의 영향으로 나타나기 때문에 비선형성이 매우 강하다. 따라서 역학 모형을 통해 예측된 강수의 보정은 비선형 모형인 인공 신경망 등을 통해 가능할 것이지만, 인공 신경망의 경우 초기 가중치 선택, 지역 최소화 문제, 뉴런의 수 결정 등의 문제로 인한 한계가 있다. 그러므로 본 연구에서는 가장 보편적으로 사용되는 다중 선형 회귀 모형을 이용하여 CGCM에 의해 모사된 강수를 보정하였으며, 예측성을 살펴보았다. 이를 위하여 우선 PNU/CME 접합 대순환 모형(Coupled General Circulation model, CGCM)(박혜선과 안중배, 2004)을 이용하여 1979년부터 2005년까지 매해 4월부터 8월까지 5개월간 앙상블 적분을 하였다. 적분 결과 중 한반도를 포함한 동북아시아 지역$(110^{\circ}E-145^{\circ}E,\;25^{\circ}N-55^{\circ}N)$의 여름철인 6월(리드 2), 7월(리드 3), 8월(리드 4) 및 여름철 평균인 JJA(from June to August) 기간의 PNU/CME CGCM에 의해 모사된 강수를 보정하기 위해 다중 선형 회귀(Multiple Linear Regression, MLR)를 이용하였다. PNU/CME 접합 대순환 모형의 결과 중 강수, 500 hPa 연직 속도, 200 hPa 발산장, 지상 기온 등의 예측 인자와 관측 강수와의 선형적인 관계를 이용하여 MLR 모형을 구축하였다. 그리고 교차 검증(cross- validation)을 수행하여 PNU/CME 접합 대순환 모형의 결과와 교차 검증 결과를 비교하였다. 상관계수, 적중률 (hit rate), 오보율(false alarm rate) 그리고 Heidke 기술 점수(Heidke skill score) 등을 살펴본 바, 보정하지 않은 모형의 결과에 비해 MLR 모형을 이용하여 보정한 결과의 강수에 대한 예측성이 뛰어난 것을 알 수 있었다.

Keywords

References

  1. 박수희, 강성대, 권원태, 2005, 비선형 멀티모델 앙상블 기법을에 의한 북반구 겨울철 강수량 예측. 한국기상학회지, 41(6), 1015-1028
  2. 박혜선, 안중배, 2004, 대기-해양-해빙 접합 모형 개발과 이를 이용한 ENSO 예측 실험(I). 한국기상학회지, 40(2), 135-146
  3. 손건태, 이정형, 이순환, 류천수, 2005, 호남지역 집중호우 예측을 위한 통계 모형 개발. 한국 기상학회지, 41(6), 897-907
  4. 안중배, 박혜선, 이효신, 이우성, 김정우, 1997, 대기 및 해양 대순환 모형에 나타난 기후 표류에 관한 연구. 한국기상학회지, 33(3), 509-520
  5. 차유미, 안중배, 2005, 역학적으로 규모축소된 남한의 여름철 강수에 대한 인공신경망 보정 능력 평가. 한국기상학회지, 41(6), 1125-1135
  6. 황윤정, 안중배, 2005, 기후 표류된 CME/PNU CGCM 겨울철 기온의 인공신경망 보정 연구. 한국기상학회지, 41(6), 1067-1076
  7. Barnett, T.P. and Preisendorfer, R., 1987, Origins and levels if monthly and seasonal forecast skill for United States surface air temperature determined by canonical correlation analysis. Monthly Weather Review, 115 (9), 1825-1850 https://doi.org/10.1175/1520-0493(1987)115<1825:OALOMA>2.0.CO;2
  8. Barnston, A.G. and Ropelewski, C.F., 1992, Prediction of ENSO episodes using canonical correlation analysis. Journal of Climate, 5 (11), 1316-1345 https://doi.org/10.1175/1520-0442(1992)005<1316:POEEUC>2.0.CO;2
  9. Blandford, H.F., 1884, On the connection of the Himalaya snowfall with dry winds and seasons of drought in India. Proceedings of the Royal Society, 37 (1), 3-22
  10. Bretherton, C.S., Smith, C, and Wallace, J.M., 1992, An intercomparison of methods for finding coupled patterns in climate data. Journal of Climate, 5 (6), 541-560 https://doi.org/10.1175/1520-0442(1992)005<0541:AIOMFF>2.0.CO;2
  11. Derr, V.B. and Slutz, R.J., 1994, Prediction of El Nino events in the Pacific by means of neural network. Artificial Intelligence Applications, 8 (2), 51-63
  12. Drapper, N.R. and Smith, H., 1981, Applied regression analysis. (2nd ed.). Wiley, New York, USA, 736 p
  13. Oldenborgh, GJ., Balmaseda, M.A., Ferranti, L., Stockdale, T.N., and Anderson, D.L.T., 2005, Evaluation of atmospheric fields from the ECMWF seasonal forecasts over a 15-Year period. Journal of Climate, 18 (6), 3250-3269 https://doi.org/10.1175/JCLI3421.1
  14. Heidke, P., 1926, Berechnung des erfolges und der gute der windstarkvorhersagen im sturmwarnungsdienst. Geografiska Annaler, 8, 301-349 https://doi.org/10.2307/519729
  15. IPCC, 2001, Climate Change 2001: The Scientific Basis. In Houghton, J.T, Ding, Y., Griggs, D.J., Noguer, M., Linden, P.J., Dai, X., Maskell, K., and Johnson, C.A. (eds.), Cambridge University Press, Cambridge, UK and New York, NY, USA, 892 p
  16. Landman, W.A. and Lisa Goddard., 2001, Statistical recalibra-tion of GCM forecasts over southern Africa using model output statistics. Journal of Climate, 15 (9), 2038-2055 https://doi.org/10.1175/1520-0442(2002)015<2038:SROGFO>2.0.CO;2
  17. Levitus, S., 1982, Climatological Atlas of the world ocean. Technical Report, NOAA Professional Paper 13. US Government Office, Washington, DC, USA, 173 p
  18. Lin, H., Derome, J., and Brunet, G, 2005, Correction of GCM seasonal forecasts using the leading forced SVD patterns (solicited), European Geosciences Union, General Assembly
  19. Lorenz, E.N., 1956, Empirical orthogonal functions and statistical weather prediction. Massachusetts Institute of Technology, Department of Meteorology, Scientific Report No.l., 49 p
  20. Massie, D.R. and Rose, M.A., 1997, Predicting daily maximum temperatures using linear regression and Eta geopotential thickness forecasts. Weather and Forecasting, 12 (4), 799-807 https://doi.org/10.1175/1520-0434(1997)012<0799:PDMTUL>2.0.CO;2
  21. Semtner, A.J., Jr., 1976, A model for the thermodynamic growth of sea ice in numerical investigations of climate. Journal of Physical Oceanography, 6 (3), 379-389 https://doi.org/10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2
  22. Sokol, Z. and Rezacova D., 2000, Improvement of local categorical precipitation forecasts from an NWP model by various statistical postprocessing methods. Studia Geophysica et Geodaetica, 44 (1), 38-56 https://doi.org/10.1023/A:1022057807597
  23. Tangang, F.T., Hsieh, WW, and Tang, B., 1997, Forecasting the equatorial Pacific sea surface temperatures by neural network models. Climate Dynamics, 13 (2), 135-147 https://doi.org/10.1007/s003820050156
  24. Walker, GT, 1924, Correlation in seasonal variations of weather. IX: A further study of world weather (World Weather II). Memoirs of the Indian Meteorological Department, 24 (9), 275-332
  25. Wilks, D.S., 1995, Statistical methods in the atmosphere science. Academic Press, San Diego, USA, 467 p
  26. Landman, W.A. and Goddard, L., 2002, Statistical recalibra-tion of GCM forecasts over Southern Africa using model output statistics. Journal of Climate, 15 (15), 2038-2055 https://doi.org/10.1175/1520-0442(2002)015<2038:SROGFO>2.0.CO;2