SPATIAL BAYESIAN MODEL AVERAGING TO CALIBRATE SHORT-RANGE WEATHER FORECAST IN JAKARTA, INDONESIA
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Published:
Sep 30, 2019
Keywords:
BMA Ensemble GOP NWP Spatial BMA
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Abstract
Bayesian Model Averaging (BMA) is a statistical post-processing method to calibrate the ensemble forecasts and create more reliable predictive interval. However, BMA does not consider spatial correlation. Geostatistical Output Perturbation (GOP) considers spatial correlation among several locations altogether. It has spatial parameters that modifies the forecast output to capture spatial information. Spatial Bayesian Model Averaging (Spatial BMA) is a method which combines BMA and GOP. This method is applied to calibrate the temperature forecast at 8 stations in Indonesia that is previously predicted by Numerical Weather Prediction (NWP). Temperature forecasts of BMA are used to obtain simulated spatially correlated error that modify temperature forecasts. Spatial BMA is able to calibrate the temperature forecast better than raw ensemble whose coverage comes closer to the standard 50%. Based on Root Mean Square Error (RMSE) criteria, Spatial BMA is able to correct forecast bias NWP with RMSE value of 1.399° lower than NWP of 2.180°.
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Qona’ah, N. Q., S., S., & P., P. (2019). SPATIAL BAYESIAN MODEL AVERAGING TO CALIBRATE SHORT-RANGE WEATHER FORECAST IN JAKARTA, INDONESIA. Malaysian Journal of Science, 38(Sp2), 55–72. https://doi.org/10.22452/mjs.sp2019no2.6
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ISMI-ICTAS18 (Published)
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Berrocal, V.J, Raftery, A.E., and Gneiting, T. (2007). Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecast. Monthly Weather Review AMS, 135: 1386-1402.
BMKG. (2011). Kajian dan Aplikasi Model CCAM (Conformal Cubic Atmospheric Model) untuk Prakiraan Cuaca Jangka Pendek Menggunakan MOS (Model Output Statistics). Jakarta: Pusat Penelitian dan Pengembangan BMKG.
Draper, N.R. and Smith, H. (1992). Applied Regression Analysis Second Edition. New York: John Wiley and Sons, Inc.
Feldmann, K. (2012). Statistical Postprocessing of Ensemble Forecasts for Temperature: The Importance of Spatial Modeling. Diplomarbeit. Ruperto-Carola University of Heidelberg, Germany.
Gel, Y., Raftery, A.E., and Gneiting, T. (2004). Calibrated probabilistic mesoscale weather field forecasting: The Geostatistical Output Perturbation (GOP) method (with discussion). Journal of the American Statistical Association, 99 (467): 575–583.
Johnson, R.A. dan Wichern, D.W. (2007). Applied Multivariate Statistical Analysis 5th Edition. New Jersey: Prentice Hall.
Luthfi, M. (2017). Bayesian Model Averaging dan Geostatistical Output Perturbation untuk Prakiraan Cuaca Jangka Pendek Terkalibrasi. Thesis, Insitut Teknologi Sepuluh Nopember, Surabaya.
Park, Y.Y. (2006). Recent development of ensemble forecast system. ASEAN-ROK Cooperation Training Workshop for the Use of Numerical Weather Prediction Products, KMA, Seoul, South Korea, 93-177.
Raftery, A.E. and Zheng, Y. (2003). Discussion: Performance of Bayesian Model Averaging. Journal of the American Statistical Association, 98: 931-938.
Raftery, A.E., Gneiting, T., Balabdoui, F. and Polakowski, M. (2005). Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review AMS, 133: 1155-1174.
Schmeits, M.J. and Kok, K.J. (2010). A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging and Extended Logistic
Regression Using ECMWF Ensemble Precipitation Forecast. Monthly Weather Review AMS, 138: 4199-4211.
Tanudidjaja. (1993). Ilmu Pengetahuan Bumi dan Antariksa. Jakarta: Penerbit Departemen Pendidikan dan Kebudayaan.
Wilks, D.S. (2006). Statistical Methods in the Atmospheric Sciences 2nd Edition. Boston: Elsevier.
Wold, S., Sjӧstrӧm, M., and Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58: 109-130