RAINFALL PREDICTION USING STATISTICAL MULTI MODEL ENSEMBLE OVER SELECTED REGION IN INDONESIA

1. RAINFALL PREDICTION USING STATISTICAL MULTI MODEL ENSEMBLE OVER SELECTED REGION IN INDONESIA INTERNATIONAL WORKSHOP ON IMPLEMENTATION OF DIGITIZATION HISTORICAL DATA AND SACA&D / ICA&D AND CLIMATE ANALYSIS IN THE REGIONAL ASEAN 02 – 05 APRIL 2012 JAKARTA / BOGOR, INDONESIA FierraSetyawan R & D of BMKG fierra.setyawan@bmkg.go.id BMKG

2. Outline • Background • Data and Methods • Objective • Result • Conclusion • Introduction ClimaTools • Future Plans Research and Development Center, BMKG BMKG

3. Background Research and Development Center, BMKG BMKG

4. Bmkg as the provider climate information • The behaviour of climate (rainfall) high variability , such as shifting and changing of wet/dry season, climate extrem issues recently • Users need climate information regulary, accurate and localized • BMKG has been challenged to provide climate information • The limitation of human resources and tools to provide climate information in high resolution • Dynamical Climate Model is high technologies computation requirements  expensive resources • Statistical model as a solution to fullfill forecaster needs in local scale Research and Development Center, BMKG BMKG

5. Spatial Planning Crops Statistical Models Water resources EOF AR ANFIS HyBMG ClimaTools Filter Kalman Plantation Wave- let Non- Linier Ensemble High Res. Weather & Climate Forecasts Multi- regr. Dissemination PCA CCA Fishery Statistical Downscaling Energy & Industry AO- GCM RCM Dynamical Downscaling Hidromet. Disaster Management Numerical/Dynamical Models Tourism MM5, DARLAM, PRECIS, RegCM4, CCAM Research and Development Center, BMKG BMKG

6. Why we need ensemble forecast ? • To antcipate and to reduce the entity of climate itself (chaotic) • Ensemble forecast is a collection of several different climate models  forcaster no need to worry which one of model that fitted for one particular location especially for his location • Various ensemble methods have been introduced; such as a lagged ensemble forecasting method (Hoffman and Kalnay, 1983), breeding techniques (Toth and Kalnay, 1993), multimodelsuperensemble forecasts (Krishnamurti et al. 1999). • Dynamic models, because each different model has its own variability generated by internal dynamics (Straus and Shukla 2000); as a result, performance of a multi-model ensemble is generally more reliable/ skillful than that of a single model (Wandishin et al, 2001, Bright and Mullen 2001). Research and Development Center, BMKG BMKG

7. Data and Methods Research and Development Center, BMKG BMKG

8. Data • Rainfall Data from 12 location (Lampung, Java, South Kalimantan and South Sulawesi) • Period: 1981 – 2009 Research and Development Center, BMKG BMKG

9. Methods • Prediction Techniques • Univariate Statistical Method: • most common statistical (ARIMA), • Hybrid (ANFIS, Wavelet Transform) • Multivariate Statistical Method : Kalman Filter Research and Development Center, BMKG BMKG

10. Methodscontd. • Multi Model Ensemble : • Simple Composite Method  Simple composite of individual forecast with equal weighting Research and Development Center, BMKG BMKG

11. skill Using Taylor Diagram • Correlation Coefficient • Root Mean Square Error • Standard Deviation Hasanudin 2006 Research and Development Center, BMKG BMKG

12. objectives • To investigate statistical model univariate and multivariate in selected location (12 location) • To provide tools for local forcaster to improve quality and accuracy of climate information especially in local scale Research and Development Center, BMKG BMKG

13. Results Research and Development Center, BMKG BMKG

14. Correlation Coefficient Univariate Technique Multivariate Technique Pusat Penelitian dan Pengembangan, BMKG BMKG

15. Correlation Coefficient contd. Univariate Multivariate Research and Development Center, BMKG BMKG

16. All Years Research and Development Center, BMKG BMKG

17. All Years Research and Development Center, BMKG BMKG

18. siNGLE YEAR Hasanudin 2006 Hasanudin 2007 Pusat Penelitian dan Pengembangan, BMKG BMKG

19. conclusion • The function of Multi model ensemble is a single model and it has a better skill • Correlation value is significant rising, marching to eastern part Indonesia, from Lampung, West Java, Central Java, East Java, South Kalimantan and South Sulawesi • MME improves accuracy of climate prediction • Multivariate Statistic technique is not always has a better prediction than univariate technique Research and Development Center, BMKG BMKG

20. Introduction ClimaTools v1.0 Research and Development Center, BMKG BMKG

21. About ClimaTools v1.0 Software The ClimaTools Software is an application for processing climate data using statistical tools whether univariate or multivariate techniques. It contains tools for data processing, analysis, prediction and verification. The ClimaTools version 1.0 Software includes the following statistical packages: • Data analysis – single wavelet power spectrum and empirical orthogonal function (EOF). • PredictionTechniques – Kalman Filter technique and Canonical Correlation Analysis (CCA). • VerificationMethods – Taylor Diagram and Receiver Operating Characteristic (ROC). Research and Development Center, BMKG BMKG

23. Future plans • Spatial Climate Prediction embedded in ClimaTools • Integration Statistical Model HyBMG into ClimaTools • Optimalization of output multimodel ensemble by adjustmentusing BMA (Bayesian Model Averaging) (koreksi) Research and Development Center, BMKG BMKG

24. Thank You Visit Us http://172.19.1.191 Contact puslitbang@bmkg.go.id Research and Development Center, BMKG BMKG