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29 July 2009

CLIMATE PREDICTION IN INDONESIAN REGIONS USING SOFT COMPUTING

Seminar Nasional Proyeksi Iklim Dan Kualitas Udara 2010-2014(Juli 2009)

CLIMATE PREDICTION IN INDONESIAN REGIONS USING SOFT COMPUTING

The Houw Liong2), Plato M.Siregar1), R.Gernowo1), Heru Widodo3)
1) Science Atmosphere Division, Faculty of Earth Science and Mineral Technology, ITB
2) Physics of Complex System Division, Faculty of Mathematics and Natural Sciences, ITB
3) UPT Hujan Buatan (Weather Modification Unit), BPPT

Abstract
According to researchers in LAPAN (Indonesian Space and Aeronautics Institute) Global Circulation Model (GCM) and Limited Area Climate Model (DARLAM) can be used to predict climate in Indonesian regions. With a scenario that the concentration of CO2 will be doubled in 100 years, the temperature anomaly can be calculated. In 50 years from 1990 most Indonesian regions the very likely will raise by 0.5 to 1.0 degrees Celsius. Some regions the temperature very likely will raise by 1.0 to 1.5 degrees Celsius. This climate model cannot predict the precipitations in Indonesian regions well. It needs some modification before it can be use to predict the precipitations well.
We know that climate model can be classified as weak causality therefore the accuracy of prediction is good only for short range prediction.
WRF (weather research and forecasting), a regional numerical weather model developed by Pennsylvania State University/ National Center for Atmospheric Research (PSU/NCAR), with a horizontal grid resolution of 5 km can be used for short range prediction.
The second approach to predict climate in Indonesia is based on soft computing by knowing that the climate in Indonesian regions is influenced by four main quasi periodic cycles: Solar Activity Cycle (Sunspot Numbers Cycle), Galactic Cosmic Ray Cycle, El Nino Southern Oscillation (ENSO) Cycle, and Indian Ocean Dipole Mode (IOD) Cycle. It can be shown that solar activity cycle can be considered as primary cycle that influence other cycles.
In practice eastern Indonesian region is dominantly influenced by ENSO. When the heat pools moves to eastern Indonesian region, then rainfall in this region will be above normal. On the other hand when the heat pool leaves eastern Indonesian region and moves to Pacific Ocean then the rainfall in this region will be below normal.
During a typical Indian Ocean Dipole Mode (IOD) event the weakening and reversal of winds in the central equatorial Indian Ocean lead to the development of unusually warm sea surface temperatures in the western Indian Ocean. IOD negative means wet condition or the rainfall will be above normal along the western Indonesian region.
Precipitation in Pontianak region which represent middle Indonesian region correlated strongly with sunspot numbers cycle (solar activity cycle). Using ANFIS (Adaptive Neuro Fuzzy Inference System) we are able to predict sunspot numbers cycles about 10 years from now so that climate and extreme weather in Indonesian regions can be predicted.
Fuzzy c-means is used to classify regions that are influenced strongly by sunspot numbers (solar activity), IOD, and ENSO cycles. This method is based on fuzzy set as fuzzy c-partition of three cycles above and as cluster center. Fuzzy c-partition matrix for grouping a collection of n data set into c classes.

Keywords: climate model, ANFIS, fuzzy clustering, climate, solar activity, soft computing

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