TEMPERATURE AND HUMIDITY FORECAST VIA UNIVARIATE PARTIAL LEAST SQUARE AND PRINCIPAL COMPONENT ANALYSIS
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Published:
Sep 30, 2019
Keywords:
MOS NWP PCA PLS Temperature and Humidity
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Abstract
Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) uses Numerical Weather Prediction (NWP) for short-term weather forecast but it gives biased result. Therefore, this study implements Univariate Partial Least Square (PLS) as Model Output Statistics (MOS) for temperature and humidity forecast. This study uses the maximum temperature (Tmax), minimum temperature (Tmin), and relative humidity (RH) which are called response variables and NWP as predictor variable. The results show that the performance of the model based on Root Mean Square Error of Prediction (RMSEP) are considered to be good and intermediate. The RMSEP for Tmax in all stations is intermediate (0.9-1.2), Tmin in three stations is good (0.5-0.8), and humidity in three stations is also good (2.6-5.0). The prediction result from the PLS is more accurate than the NWP model and able to correct an 89.94% of the biased NWP for Tmin forecasting.
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How to Cite
S., S., Nisaa, Z., & Nur ‘Anisa, K. (2019). TEMPERATURE AND HUMIDITY FORECAST VIA UNIVARIATE PARTIAL LEAST SQUARE AND PRINCIPAL COMPONENT ANALYSIS. Malaysian Journal of Science, 38(Sp2), 1–13. https://doi.org/10.22452/mjs.sp2019no2.1
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ISMI-ICTAS18 (Published)
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References
BMKG. (2006). Uji Operasional dan Validasi Model Output Statistik (MOS). Jakarta: BMKG.
Boulesteix, Anne-Laure, and Strimmer, K. (2006). Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data. Journal of Briefings in Bioinformatics, 8: 32-44.
Clark, M. P., Hay, L. E., and Whitaker, J. S. (2001). Development of operational hydrologic forecasting capabilities. American Geophysical Union, Fall Meeting.
Glahn, H. R., and Lowry, D. A. (1972). The Use of a Model Output Statistics (MOS) in Objective Weather Forecasting. Applied Meteorology, 1203-1211.
Johnson, R. A., and Winchern, D. W. (2007). Applied Multivariate Statistical Analysis 6th Edition. United States: Pearson Education.
Joliffe, I. T. (1986). Principal Component Analysis (2nd ed.). New York: Springer-Verlag.
Tjasyono, B. (2004). Klimatologi 2nd Edition. Bandung: ITB.
Wardani, I. K. (2010). Manfaat Prediksi Cuaca Jangka -Pendek Berdasarkan Data Ra-diosonde dan Numerical Weather Prediction (NWP) untuk Pertanian Daerah.
Wilks, D. S. (2006). Statistical Methods in the Atmospheric Sciences 2nd Edition. Boston: Elvesier.
Wold, S., Sjostrom, M., and Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Journal of Chemometrics and Intelligent Systems, 58: 109-130.
Boulesteix, Anne-Laure, and Strimmer, K. (2006). Partial Least Squares: A Versatile Tool for the Analysis of High-Dimensional Genomic Data. Journal of Briefings in Bioinformatics, 8: 32-44.
Clark, M. P., Hay, L. E., and Whitaker, J. S. (2001). Development of operational hydrologic forecasting capabilities. American Geophysical Union, Fall Meeting.
Glahn, H. R., and Lowry, D. A. (1972). The Use of a Model Output Statistics (MOS) in Objective Weather Forecasting. Applied Meteorology, 1203-1211.
Johnson, R. A., and Winchern, D. W. (2007). Applied Multivariate Statistical Analysis 6th Edition. United States: Pearson Education.
Joliffe, I. T. (1986). Principal Component Analysis (2nd ed.). New York: Springer-Verlag.
Tjasyono, B. (2004). Klimatologi 2nd Edition. Bandung: ITB.
Wardani, I. K. (2010). Manfaat Prediksi Cuaca Jangka -Pendek Berdasarkan Data Ra-diosonde dan Numerical Weather Prediction (NWP) untuk Pertanian Daerah.
Wilks, D. S. (2006). Statistical Methods in the Atmospheric Sciences 2nd Edition. Boston: Elvesier.
Wold, S., Sjostrom, M., and Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Journal of Chemometrics and Intelligent Systems, 58: 109-130.