RAINFALL FORECAST WITH BEST AND FULL MEMBERS OF THE NORTH AMERICAN MULTI-MODEL ENSEMBLE
Article Sidebar
Published:
Sep 30, 2019
Keywords:
BMA calibration NMME
Main Article Content
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
.
Downloads
Download data is not yet available.
Article Details
How to Cite
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
Section
ISMI-ICTAS18 (Published)
Transfer of Copyrights
- In the event of publication of the manuscript entitled [INSERT MANUSCRIPT TITLE AND REF NO.] in the Malaysian Journal of Science, I hereby transfer copyrights of the manuscript title, abstract and contents to the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) for the full legal term of copyright and any renewals thereof throughout the world in any format, and any media for communication.
Conditions of Publication
- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures, maps, photographs and other visual materials of whom the copyrights is owned by a third party.
- This manuscript contains no statements that are contradictory to the relevant local and international laws or that infringes on the rights of others.
- I agree to indemnify the Malaysian Journal of Science and the Faculty of Science, University of Malaya (as the publisher) in the event of any claims that arise in regards to the above conditions and assume full liability on the published manuscript.
Reviewer’s Responsibilities
- Reviewers must treat the manuscripts received for reviewing process as confidential. It must not be shown or discussed with others without the authorization from the editor of MJS.
- Reviewers assigned must not have conflicts of interest with respect to the original work, the authors of the article or the research funding.
- Reviewers should judge or evaluate the manuscripts objective as possible. The feedback from the reviewers should be express clearly with supporting arguments.
- If the assigned reviewer considers themselves not able to complete the review of the manuscript, they must communicate with the editor, so that the manuscript could be sent to another suitable reviewer.
Copyright: Rights of the Author(s)
- Effective 2007, it will become the policy of the Malaysian Journal of Science (published by the Faculty of Science, University of Malaya) to obtain copyrights of all manuscripts published. This is to facilitate:
- Protection against copyright infringement of the manuscript through copyright breaches or piracy.
- Timely handling of reproduction requests from authorized third parties that are addressed directly to the Faculty of Science, University of Malaya.
- As the author, you may publish the fore-mentioned manuscript, whole or any part thereof, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given. You may produce copies of your manuscript, whole or any part thereof, for teaching purposes or to be provided, on individual basis, to fellow researchers.
- You may include the fore-mentioned manuscript, whole or any part thereof, electronically on a secure network at your affiliated institution, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- You may include the fore-mentioned manuscript, whole or any part thereof, on the World Wide Web, provided acknowledgement regarding copyright notice and reference to first publication in the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers) are given.
- In the event that your manuscript, whole or any part thereof, has been requested to be reproduced, for any purpose or in any form approved by the Malaysian Journal of Science and Faculty of Science, University of Malaya (as the publishers), you will be informed. It is requested that any changes to your contact details (especially e-mail addresses) are made known.
Copyright: Role and responsibility of the Author(s)
- In the event of the manuscript to be published in the Malaysian Journal of Science contains materials copyrighted to others prior, it is the responsibility of current author(s) to obtain written permission from the copyright owner or owners.
- This written permission should be submitted with the proof-copy of the manuscript to be published in the Malaysian Journal of Science
References
Barnston, A.G., Tippett, M.K., L’Heureux, M.L., Li, S. and DeWitt, D.G. (2011). Skill of real-time seasonal ENSO model predictions during 2002–11: Is our capability increasing?. Bulletin of the American Meteorological Society, 93: 631-651.
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.
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.