OJS  Vol.4 No.9 , October 2014
Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall
ABSTRACT
Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1st grid to 64th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

Cite this paper
Sahriman, S. , Djuraidah, A. and Wigena, A. (2014) Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall. Open Journal of Statistics, 4, 678-686. doi: 10.4236/ojs.2014.49063.
References
[1]   Notodiputro, K.A., Wigena, A.H. and Fitriadi (2005) Principal Component Regression Approach and ARIMA to Statistical Downscaling. Journal of Science and Technology, 11, 137-142.

[2]   Fernandez, E. (2005) On the Influence of Predictors Area in Statistical Downscaling of Daily Parameters. Norwegia Meteorological Institute, 9, 1-21.

[3]   Busuioc, A., Chen, D. and Hellstrom, C. (2001) Performance of Statistical Downscaling Models in GCM Validation and Regional Climate Change Estimates: Application for Swedish Precipitation. International Journal of Climatology, 21, 557-578.
http://dx.doi.org/10.1002/joc.624

[4]   Wigena, A.H. (2011) Multirespon Partial Least Squares Regression Method for Statistical Downscaling. Statistics and Computation Forums, 16, 12-15.

[5]   Warawati, A.D. (2013) Sukadana Station Rainfall Forecasting Using Statistical Downscaling Technique Based on TRMM Satellite Data. Thesis, Bogor Agricultural University (in Indonesian), Indonesia.

[6]   Wigena, A.H. (2006) Modeling of Statistical Downscaling Using Projection Pursuit Regression for Forecasting Monthly Rainfall. Doctoral Dissertation, Bogor Agricultural University (in Indonesian), Indonesia.

[7]   Jollife, I.T. (2002) Principal Component Analysis. Springer-Verlag, New York.

[8]   Wold, S., Sjostrom, M. and Eriksson, L. (2001) PLS-Regression: A Basic Tool of Chemometrics. Chemometrics and Intelligent Laboratory Systems, 58, 109-130.
http://dx.doi.org/10.1016/S0169-7439(01)00155-1

 
 
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