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RAINFALL PREDICTION USING STATISTICAL MULTI MODEL ENSEMBLE OVER SELECTED REGION IN INDONESIA INTERNATIONAL WORKSHOP ON IMPLEMENTATION OF DIGITIZATION HISTORICAL.

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Presentation on theme: "RAINFALL PREDICTION USING STATISTICAL MULTI MODEL ENSEMBLE OVER SELECTED REGION IN INDONESIA INTERNATIONAL WORKSHOP ON IMPLEMENTATION OF DIGITIZATION HISTORICAL."— Presentation transcript:

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 Fierra Setyawan R & D of BMKG BMKG

2 OUTLINE  Background  Data and Methods  Objective  Result  Conclusion  Introduction ClimaTools  Future Plans BMKG Research and Development Center, BMKG

3 BACKGROUND BMKG

4 BMKG AS THE PROVIDER CLIMATE INFORMATION BMKG Research and Development Center, BMKG  The behaviour of climate (rainfall)  high variability, such as shifting and changing of wet/dry season, climate extrem issues recently regulary, accurate and localized  Users need climate information regulary, accurate and localized provide climate information  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  Statistical model as a solution to fullfill forecaster needs in local scale

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

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), multimodel superensemble 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). BMKG Research and Development Center, BMKG

7 DATA AND METHODS BMKG Research and Development Center, BMKG

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

9 METHODS BMKG Prediction Techniques – Univariate Statistical Method: most common statistical (ARIMA), Hybrid (ANFIS, Wavelet Transform) – Multivariate Statistical Method : Kalman Filter

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

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

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

13 RESULTS BMKG Research and Development Center, BMKG

14 BMKG Pusat Penelitian dan Pengembangan, BMKG Univariate Technique Multivariate Technique CORRELATION COEFFICIENT

15 UnivariateMultivariate BMKG Research and Development Center, BMKG CORRELATION COEFFICIENT CONTD.

16 BMKG Research and Development Center, BMKG ALL YEARS

17 BMKG Research and Development Center, BMKG

18 BMKG Pusat Penelitian dan Pengembangan, BMKG SINGLE YEAR Hasanudin 2006 Hasanudin 2007

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 BMKG Research and Development Center, BMKG

20 BMKG INTRODUCTION CLIMATOOLS V1.0

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).  Prediction Techniques – Kalman Filter technique and Canonical Correlation Analysis (CCA).  Verification Methods – Taylor Diagram and Receiver Operating Characteristic (ROC). BMKG Research and Development Center, BMKG

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23 FUTURE PLANS BMKG Research and Development Center, BMKG  Spatial Climate Prediction embedded in ClimaTools  Integration Statistical Model HyBMG into ClimaTools  Optimalization of output multimodel ensemble by adjustment using BMA (Bayesian Model Averaging) (koreksi)

24 THANK YOU BMKG Research and Development Center, BMKG Visit Us


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