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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 - PowerPoint PPT Presentation
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RAINFALL PREDICTION USING RAINFALL PREDICTION USING STATISTICAL MULTI MODEL STATISTICAL MULTI MODEL ENSEMBLE OVER SELECTED ENSEMBLE OVER SELECTED
REGION IN INDONESIAREGION 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 SetyawanR & D of BMKG
BMKG
OUTLINEOUTLINE Background Data and Methods Objective Result Conclusion Introduction ClimaTools Future Plans
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
BACKGROUNDBACKGROUND
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
BMKG AS THE PROVIDER BMKG AS THE PROVIDER CLIMATE INFORMATIONCLIMATE INFORMATION
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
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 regulary, accurate and localizedlocalized
BMKG has been challenged to provide climate informationprovide 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
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
ARAR
Wave-let
FilterFilterKalmanKalman
ANFIS
EOF
AO-AO-GCMGCM
Multi-regr.
CCA PCA
Non-Linier
RCMRCM
Numerical/Dynamical ModelsNumerical/Dynamical Models
Statistical Models
EnsembleEnsemble
High Res.High Res.Weather &Weather &
ClimateClimateForecastsForecastsStatistical
Downscaling
DynamicalDownscaling
SpatialPlanning
Crops
Waterresources
Plantation
Fishery
Energy &Industry
Hidromet.Disaster
ManagementTourismMM5, DARLAM, PRECIS, RegCM4, MM5, DARLAM, PRECIS, RegCM4,
CCAMCCAM
HyBMGHyBMGClimaToolsClimaTools
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).
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
DATA AND METHODSDATA AND METHODS
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
DATADATA
Rainfall Data from 12 location (Lampung, Java, South Kalimantan and South Sulawesi)
Period:
1981 – 2009
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
METHODSMETHODS
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• Prediction Techniques– Univariate
Statistical Method: most common statistical (ARIMA), Hybrid (ANFIS, Wavelet Transform)
– Multivariate Statistical Method : Kalman Filter
METHODSMETHODS CONTD.CONTD.
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
• Multi Model Ensemble : Simple Composite Method Simple composite of individual forecast with equal weighting
i
iFMP
1
SKILLSKILL
Using Taylor Diagram Correlation Coefficient Root Mean Square Error Standard Deviation
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
Hasanudin 2006
OBJECTIVESOBJECTIVES
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, 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
RESULTSRESULTS
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
BMKGBMKGPusat Penelitian dan Pengembangan, BMKGPusat Penelitian dan Pengembangan, BMKG
Univariate Technique Multivariate Technique
CORRELATION COEFFICIENTCORRELATION COEFFICIENT
Univariate Multivariate
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CORRELATION COEFFICIENT CORRELATION COEFFICIENT CONTD.CONTD.
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
ALL YEARSALL YEARS
ALL YEARSALL YEARS
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
BMKGBMKGPusat Penelitian dan Pengembangan, BMKGPusat Penelitian dan Pengembangan, BMKG
SINGLE YEARSINGLE YEAR
Hasanudin 2006Hasanudin 2006 Hasanudin 2007Hasanudin 2007
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
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
INTRODUCTION CLIMATOOLSINTRODUCTION CLIMATOOLS V1.0 V1.0
ABOUT CLIMATOOLS ABOUT CLIMATOOLS V1.0 V1.0 SOFTWARESOFTWAREThe 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).
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
FUTURE PLANSFUTURE PLANS
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, 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)
THANK YOUTHANK YOU
BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG
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