CALIBRATING WEATHER FORECASTING IN INDONESIA: THE GEOSTATISTICAL OUTPUT PERTURBATION METHOD
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Published:
Sep 30, 2019
Keywords:
GOP NWP spatial weather forecasting
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Abstract
The Numerical Weather Prediction (NWP) was developed by the Meteorological, Climatological, and Geophysical Agency in Indonesia for the purpose of weather forecasting, however, it comes with a high level of bias. This purpose of this study therefore was to improve this model with the use of Geostatistical Output Perturbation (GOP), implemented in the conformal-cubic atmospheric model (CCAM) on NWP data from the eight meteorological stations in Indonesia, i.e. Kemayoran, Priok, Cengkareng, Pondok Betung, Curug, Dermaga, Tangerang and Citeko stations. The findings indicated exponential as the best distribution model for analyzing temperature in Indonesia using GOP. Also, locations which are considerably far away from other locations could have significant impact on the accuracy of the weather forecasts. In this case, Citeko station has quite different characteristics location considering the fact that it is located on higher elevation compared with other stations. Therefore, the exclusion of Citeko station produced better forecasting in terms of accuracy and precision, increasing to about twice the result when the station was included in the analysis.
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S., S., P., P., Mukhlash, I., Nur ‘Anisa, K., Haryoko, U., & Harsa, H. (2019). CALIBRATING WEATHER FORECASTING IN INDONESIA: THE GEOSTATISTICAL OUTPUT PERTURBATION METHOD. Malaysian Journal of Science, 38(Sp2), 100–112. https://doi.org/10.22452/mjs.sp2019no2.9
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ISMI-ICTAS18 (Published)
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References
Anggraeni, D. (2013). Calibration forecasting rainfall ensemble data using output statistic model ensemble (EMOS) and Bayesian model averaging (BMA). Surabaya: Thesis, Institut Teknologi Sepuluh Nopember.
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publisher.
Berrocal, V., Raftery, A., and Gneiting, T. (2007). Combining spatial statistical and ensemble information in probabilistic weather forecast. Monthly Weather Review AMS, 135: 1386-1402.
BMKG. (2011). Study and model application of CCAM (conformal-cubic atmospheric model) model for short term weather forecast using MOS (model output statistics). Jakarta: Research and Development Center BMKG.
Cliff, A., and Ord, J. (1981). Spatial Processes: Models & Applications. London: Pion Limited.
Cressie, N. (1985). Fitting variogram models by weighted least squares. Mathematical Geology, 5(17): 563–585.
Cressie, N. (1993). Statistics for Spatial Data Revised Edition. New Jersey: John Wiley and Sons, Inc.
Feldmann, K. (2012). Statistical Postprocessing of Ensemble Forecasts for Temperature: The Importance of Spatial Modeling. Germany: Diplomarbeit, Ruperto-Carola University of Heidelberg.
Gel, Y., Raftery, A., 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.
Karl, J. W., and Maurer, B. A. (2010). Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information. Ecological Informatics, 5(3): 194–202.
Luthfi, M., Sutikno, and Purhadi. (2018). Calibrating weather forecast using Bayesian model averaging and geostatistical output perturbation. Journal of Science, 3(1): 1-9.
Moller, A. (2014). Multivariate and spatial ensemble postprocessing methods. Germany: Dissertation. Ruperto-Carola University of Heidelberg.
Narendra, R. D., Sutikno, and Purhadi. (2017). Ensemble model output statistics for short-term weather forecast. In ICoMSE 2017: The 1st Annual International Conference on Mathematics, Science, and Education. Malang, Indonesia.
Safitri, R., and Sutikno. (2012). Model output statistics with projection pursuit regression to predict minimum, maximum temperature and humidity. Jurnal Sains dan Seni ITS, 1(1): 2301-928X.
Tanudidjaja. (1993). Earth and Space Science. Jakarta: Publisher Department of Education and Culture.
Tjasyono, B., and Harijono, S. (2008). Indonesian Meteorology Model 2 of Clouds and Monsoon Rain. Meteorology and Geophysics Agency Jakarta.
Wilks, D. (2006). Statistical Methods in the Atmospheric Sciences 2nd Edition. Boston: Elsevier.
Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publisher.
Berrocal, V., Raftery, A., and Gneiting, T. (2007). Combining spatial statistical and ensemble information in probabilistic weather forecast. Monthly Weather Review AMS, 135: 1386-1402.
BMKG. (2011). Study and model application of CCAM (conformal-cubic atmospheric model) model for short term weather forecast using MOS (model output statistics). Jakarta: Research and Development Center BMKG.
Cliff, A., and Ord, J. (1981). Spatial Processes: Models & Applications. London: Pion Limited.
Cressie, N. (1985). Fitting variogram models by weighted least squares. Mathematical Geology, 5(17): 563–585.
Cressie, N. (1993). Statistics for Spatial Data Revised Edition. New Jersey: John Wiley and Sons, Inc.
Feldmann, K. (2012). Statistical Postprocessing of Ensemble Forecasts for Temperature: The Importance of Spatial Modeling. Germany: Diplomarbeit, Ruperto-Carola University of Heidelberg.
Gel, Y., Raftery, A., 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.
Karl, J. W., and Maurer, B. A. (2010). Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information. Ecological Informatics, 5(3): 194–202.
Luthfi, M., Sutikno, and Purhadi. (2018). Calibrating weather forecast using Bayesian model averaging and geostatistical output perturbation. Journal of Science, 3(1): 1-9.
Moller, A. (2014). Multivariate and spatial ensemble postprocessing methods. Germany: Dissertation. Ruperto-Carola University of Heidelberg.
Narendra, R. D., Sutikno, and Purhadi. (2017). Ensemble model output statistics for short-term weather forecast. In ICoMSE 2017: The 1st Annual International Conference on Mathematics, Science, and Education. Malang, Indonesia.
Safitri, R., and Sutikno. (2012). Model output statistics with projection pursuit regression to predict minimum, maximum temperature and humidity. Jurnal Sains dan Seni ITS, 1(1): 2301-928X.
Tanudidjaja. (1993). Earth and Space Science. Jakarta: Publisher Department of Education and Culture.
Tjasyono, B., and Harijono, S. (2008). Indonesian Meteorology Model 2 of Clouds and Monsoon Rain. Meteorology and Geophysics Agency Jakarta.
Wilks, D. (2006). Statistical Methods in the Atmospheric Sciences 2nd Edition. Boston: Elsevier.