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
RAINFALL PREDICTION USING
STATISTICAL MULTI MODEL
ENSEMBLE OVER SELECTED REGION
IN INDONESIA
BMKG
Fierra Setyawan
R & D of BMKG
fierra.setyawan@bmkg.go.id
OUTLINE
Background
Data and Methods
Objective
Result
Conclusion
Introduction ClimaTools
Future Plans
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BACKGROUND
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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
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Spatial
Planning
Statistical Models
AR
WaveMulti- let
regr.
CCA
Water
resources
EOF
ANFIS
Filter
Kalman
PCA
HyBMG
ClimaTools
NonLinier
Ensemble
Statistical
Downscaling
AOGCM
Plantation
High Res.
Weather &
Climate
Forecasts
Fishery
Energy &
Industry
RCM
Dynamical
Downscaling
Hidromet.
Disaster
Management
Numerical/Dynamical Models
MM5, DARLAM, PRECIS, RegCM4, CCAM
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Crops
Tourism
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).
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DATA AND METHODS
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DATA
 Rainfall Data from 12
location (Lampung,
Java, South
Kalimantan and
South Sulawesi)
 Period:
1981 – 2009
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METHODS
• Prediction Techniques
– Univariate Statistical
Method:
most common
statistical (ARIMA),
Hybrid (ANFIS,
Wavelet Transform)
– Multivariate Statistical
Method : Kalman Filter
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METHODS CONTD.
• Multi Model Ensemble :
Simple Composite Method  Simple
composite of individual forecast with equal
weighting
1

P
F

M
i
i
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SKILL
Using Taylor Diagram
Correlation Coefficient
Root Mean Square Error
Standard Deviation
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Hasanudin 2006
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
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RESULTS
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CORRELATION COEFFICIENT
Univariate Technique
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Multivariate Technique
CORRELATION COEFFICIENT
Univariate
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Multivariate
CONTD.
ALL YEARS
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ALL YEARS
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SINGLE YEAR
Hasanudin 2006
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Hasanudin 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
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INTRODUCTION CLIMATOOLS V1.0
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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).
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FUTURE 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)
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THANK YOU
Visit Us
http://172.19.1.191
Contact
puslitbang@bmkg.go.id
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