SPATIAL BAYESIAN MODEL AVERAGING TO CALIBRATE SHORT-RANGE WEATHER FORECAST IN JAKARTA, INDONESIA
Article Sidebar
Published:
Sep 30, 2019
Keywords:
BMA Ensemble GOP NWP Spatial BMA
Main Article Content
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°.
Downloads
Download data is not yet available.
Article Details
How to Cite
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
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- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures,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
Anggraeni, D. (2013). Kalibrasi Peramalan Ensemble Data Curah Hujan Dengan Metode Ensemble Model Output Statistics (EMOS) dan Bayesian Model Averaging (BMA). Tesis. Insitut Teknologi Sepuluh Nopember, Surabaya.
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
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