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

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

[email protected]

BMKG

OUTLINEOUTLINE Background Data and Methods Objective Result Conclusion Introduction ClimaTools Future Plans

BMKGBMKGResearch and Development CenterResearch and Development Center, BMKG, BMKG

BACKGROUNDBACKGROUND


BMKG AS THE PROVIDER BMKG AS THE PROVIDER CLIMATE INFORMATIONCLIMATE 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 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


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).


DATA AND METHODSDATA AND METHODS


DATADATA

Rainfall Data from 12 location (Lampung, Java, South Kalimantan and South Sulawesi)

Period:

1981 – 2009


METHODSMETHODS


• Prediction Techniques– Univariate

Statistical Method: most common statistical (ARIMA), Hybrid (ANFIS, Wavelet Transform)

– Multivariate Statistical Method : Kalman Filter

METHODSMETHODS CONTD.CONTD.


• 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


Hasanudin 2006

OBJECTIVESOBJECTIVES


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


BMKGBMKGPusat Penelitian dan Pengembangan, BMKGPusat Penelitian dan Pengembangan, BMKG

Univariate Technique Multivariate Technique

CORRELATION COEFFICIENTCORRELATION COEFFICIENT

Univariate Multivariate


CORRELATION COEFFICIENT CORRELATION COEFFICIENT CONTD.CONTD.


ALL YEARSALL YEARS

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



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).


FUTURE PLANSFUTURE PLANS


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


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http://172.19.1.191http://172.19.1.191

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

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