RAINFALL FORECAST WITH BEST AND FULL MEMBERS OF THE NORTH AMERICAN MULTI-MODEL ENSEMBLE
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Published:
Sep 30, 2019
Keywords:
BMA calibration NMME
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Abstract
The North American Multi-Model Ensemble (NMME) is a multi-model seasonal forecasting system consisting of models from combined US modelling centres. The NMME is expected to generate better rainfall prediction than a single model. However, the NMME forecasts are underdispersive or overdispersive, and calibration is needed to produce more accurate forecasting. This research examined the monthly rainfall data in Surabaya generated by nine NMME models and further calibrated them with bayesian model averaging (BMA). The purpose of this research was to assess the performance of the calibration results using the best four models and the full ensemble. The four models are CanCM3, CanCM4, CCSM3, and CCSM4, which were selected based on their skills. Both calibration results were evaluated using the continuous range probability score (CRPS) and the percentage of captured observations. The calibration with four models produced an average CRPS of 6.27 with 88.16% coverage, while with nine models an average CRPS of 5.23 with 92.11% coverage was obtained. This result suggests using the full ensemble to generate more accurate probabilistic forecasts
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Faidah, D. Y., Kuswanto, H., S., S., & Ferawati, K. (2019). RAINFALL FORECAST WITH BEST AND FULL MEMBERS OF THE NORTH AMERICAN MULTI-MODEL ENSEMBLE. Malaysian Journal of Science, 38(Sp2), 113–119. https://doi.org/10.22452/mjs.sp2019no2.10
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ISMI-ICTAS18 (Published)
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References
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Becker, E., Van den Dool, D. & Zhang, Q. (2014). Predictability and forecast skill in NMME. Journal of Climate, 27(15): 5891-5906.
Doblas-Reyes, F.J., Hagedorn, R. & Palmer, T.N. (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A, 57: 234-252.
Gneiting, T., Raftery, A.E., Westveld III, A.H. & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 135(5): 1098-1118.
Hagedorn, R., Doblas-Reyes, F.J. & Palmer, T.N. (2004). The rationale behind the success of multi-model ensembles in seasonal forecasting. Tellus A, 57: 219-233.
Hamill, T.M. & Colucci, S.J. (1997). Verification of Eta-RSM short-range ensemble forecast. Monthly Weather Forecast, 125: 1312-1327.
Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15: 59-570.
Kirtman, B.P., Min, D., Infanti, J.M., Kinter, J.L., Paolino, D.A., Zhang, Q. & Wood, E.F. (2014). The North American Multi Model Ensemble (NMME); Phase-1, Seasonal-to- Interannual Prediction; Phase-2, toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95(4): 585-601.
Kuswanto, H. (2010). New calibration method for ensemble forecast of non-normally distributed climate variables using meta-gaussian distribution. In: Chaerun, S.K. & Ihsanawati. (eds.): Science for Sustainable Development, Proceeding of the Third International Conference on Mathematics and Natural Sciences, Bandung, 23-25 November, pp. 932-939.
Kuswanto, H. & Sari, M.R. (2013). Bayesian model averaging with markov chain monte carlo for calibrating temperature forecast from combination of time series model. Journal of Mathematics and Statistics, 9(4): 349-356.
Ma, F., Ye, A., Deng, X., Zhou, Z., Liu, X., Duan, Q. & Gong, W. (2015). Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China. International Journal of Climatology, 36(1): 132-144.
Palmer, T.N.A. (2001). Nonlinear dynamical perspective on model error: a proposal for nonlocal stochastic–dynamic parameterization in weather and climate prediction models. Journal Meteorological Society, 127: 685-708.
Raftery, A.E., Gneiting, T., Balabdoul, F. & Polakowski, M. (2005). Using bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133: 1155-1174.
Sloughter, J.M., Gneiting, T., Raftery, A.E. (2010). Probabilistic wind speed forecasting using ensembels and bayesian model averaging. Journal of the American Statistical Association, 105(489): 25-35.
Smith, D.M., Scaife, A.A., Boer, G.J., Caian, M., Doblas-Reyes, F.J., Guemas, V. & Wyser, K. (2013). Real-time multi-model decadal climate predictions. Climate Dynamic, 41: 2875-2888.
Wang, S., Zhang, N., Wu, L. & Wang, Y. (2016). Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew Energy, 94: 629-36.
Yuan, X. & Wood, E.F. (2013). Multimodel seasonal forecasting of global drought onset. Geophysical Research, 40(18): 4900-4905.
Bao, L., Gneiting, T., Grimit, E.P., Guttorp, P. & Raftery, A.E. (2013). Bias correction and bayesian model averaging for ensemble forecasts of surface wind direction. Monthly Weather Review, 138: 1811-1821.
Becker, E., Van den Dool, D. & Zhang, Q. (2014). Predictability and forecast skill in NMME. Journal of Climate, 27(15): 5891-5906.
Doblas-Reyes, F.J., Hagedorn, R. & Palmer, T.N. (2005). The rationale behind the success of multi-model ensembles in seasonal forecasting-II calibration and combination. Tellus A, 57: 234-252.
Gneiting, T., Raftery, A.E., Westveld III, A.H. & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 135(5): 1098-1118.
Hagedorn, R., Doblas-Reyes, F.J. & Palmer, T.N. (2004). The rationale behind the success of multi-model ensembles in seasonal forecasting. Tellus A, 57: 219-233.
Hamill, T.M. & Colucci, S.J. (1997). Verification of Eta-RSM short-range ensemble forecast. Monthly Weather Forecast, 125: 1312-1327.
Hersbach, H. (2000). Decomposition of the continuous ranked probability score for ensemble prediction systems. Weather and Forecasting, 15: 59-570.
Kirtman, B.P., Min, D., Infanti, J.M., Kinter, J.L., Paolino, D.A., Zhang, Q. & Wood, E.F. (2014). The North American Multi Model Ensemble (NMME); Phase-1, Seasonal-to- Interannual Prediction; Phase-2, toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95(4): 585-601.
Kuswanto, H. (2010). New calibration method for ensemble forecast of non-normally distributed climate variables using meta-gaussian distribution. In: Chaerun, S.K. & Ihsanawati. (eds.): Science for Sustainable Development, Proceeding of the Third International Conference on Mathematics and Natural Sciences, Bandung, 23-25 November, pp. 932-939.
Kuswanto, H. & Sari, M.R. (2013). Bayesian model averaging with markov chain monte carlo for calibrating temperature forecast from combination of time series model. Journal of Mathematics and Statistics, 9(4): 349-356.
Ma, F., Ye, A., Deng, X., Zhou, Z., Liu, X., Duan, Q. & Gong, W. (2015). Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China. International Journal of Climatology, 36(1): 132-144.
Palmer, T.N.A. (2001). Nonlinear dynamical perspective on model error: a proposal for nonlocal stochastic–dynamic parameterization in weather and climate prediction models. Journal Meteorological Society, 127: 685-708.
Raftery, A.E., Gneiting, T., Balabdoul, F. & Polakowski, M. (2005). Using bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133: 1155-1174.
Sloughter, J.M., Gneiting, T., Raftery, A.E. (2010). Probabilistic wind speed forecasting using ensembels and bayesian model averaging. Journal of the American Statistical Association, 105(489): 25-35.
Smith, D.M., Scaife, A.A., Boer, G.J., Caian, M., Doblas-Reyes, F.J., Guemas, V. & Wyser, K. (2013). Real-time multi-model decadal climate predictions. Climate Dynamic, 41: 2875-2888.
Wang, S., Zhang, N., Wu, L. & Wang, Y. (2016). Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renew Energy, 94: 629-36.
Yuan, X. & Wood, E.F. (2013). Multimodel seasonal forecasting of global drought onset. Geophysical Research, 40(18): 4900-4905.