Statistical climate modelling

January 15, 2018 | Author: Anonymous | Category: Math, Statistics And Probability, Statistics
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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 [email protected]

OUTLINE Background Data and Methods Objective Result Conclusion

Introduction ClimaTools Future Plans

Research and Development Center, BMKG BMKG

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

Research and Development Center, BMKG BMKG

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

Research and Development Center, BMKG BMKG

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

[email protected]

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