CALIBRATING WEATHER FORECASTING IN INDONESIA: THE GEOSTATISTICAL OUTPUT PERTURBATION METHOD
Article Sidebar
Published:
Sep 30, 2019
Keywords:
GOP NWP spatial weather forecasting
Main Article Content
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.
Downloads
Download data is not yet available.
Article Details
How to Cite
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
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- I hereby state that this manuscript to be published is an original work, unpublished in any form prior and I have obtained the necessary permission for the reproduction (or am the owner) of any images, illustrations, tables, charts, figures,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:
(a) Protection against copyright infringement of the manuscript through copyright breaches or piracy.
(b) 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). 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.