Fuzzy Clustering and Extreme Weather/Climate in Indonesia

8/14/2019 Fuzzy Clustering and Extreme Weather/Climate in Indonesia

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Fuzzy Clustering and Extreme Weather/Climate in Indonesia

Plato M.Siregar1) and The Houw Liong2)

1) Faculty of Earth Science and Technology, ITB2) Faculty of Mathematics and Natural Sciences, ITB

Abstract

Extreme weather/climate in Indonesia is influenced by three cycles: El Nino Southern Oscillation (ENSO), Sunspot Numbers and Indian

Ocean Dipole Mode (IOD). East Indonesian regions are dominantly influenced by ENSO. 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 (SST) in the western Indian Ocean. IOD negative means wet condition along the coast of Sumatra (west Indonesians regions)

Precipitation in Pontianak regions (middle Indonesian regions) correlated strongly with sunspot numbers (solar activity cycle).

Fuzzy c-means is used to classify regions that are influenced strongly by sunspot numbers (solar activities), 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 ofn data set in to c classes, we define object function for fuzzy as Euclidian distance. The results show that Jakarta region is similar 0.6 to solar

cycle, below 0.5 to IOD cycle, and 0.5 to ENSO cycle.

This study also explores the refined classifications and its physical interpretations.

Presented in KAGI-21, The International Workshop on Regional Models for Prediction of Tropical Weather and Climate, Bandung , 2006.


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Solar Activities and Extreme Climate

The results of researches in Indonesian regions have shown the existence of correlations among sun activities with Quasi-Biennial

Oscillation, surface temperature, rainfall, tree ring, and isobar height of atmosphere layers (Ratag, 1994, 1999). Indonesia passed by equator

hence becomes a source of sensible heat that plays an important part in global circulations. Latent heat will be released when aqueous vapour

turn into clouds, especially when strong convective atmosphere of inter tropical convergence zone residing above Indonesian region.

Winter Monsoon in Northern Hemisphere (Asian Monsoon) and Southern Hemisphere (Australian Monsoon) influenced by spreading of

source or complex hot release on Asia and its surrounding. Now, it has been revealed that Indonesian regions are keys in Southern Oscillation.

Indonesia is passed by equator which means it is warm and heavy rainfall. It is an important area in world in cases of tropical-equatorial

convection process and atmosphere circulation is very different from extra-tropics area (Ratag, 1999).

The seasonal weather process tend to be modulated by long-range weather cycle, for example ENSO (El Nio-Southern Oscillation) andQBO (Quasi Biennial Oscillation) related to mechanism eleven annual year or its harmonic not yet been known as detail.Murakami et al had

tried to cluster the climate regions of Indonesia as follows: SEAM (South East Asia Monsoon), MC (Maritime Continent), and NAIM (North

Australia-Indonesia Monsoon) base on outgoing long wave radiation patterns.

If we consider the influence of solar activity average to cluster rainfall patterns on Indonesian regions then they correspond to patterns ofmonthly rainfall average as in figure 1, but if we have to consider ten to twelve year variations in solar activities to know extreme climate then

clustering has to be based on yearly rainfalls as the time series of rainfalls show that in middle Indonesia rainfall patterns are mainly influenced

by sunspot cycle ( figure 2), in East Indonesia are mainly influenced by ENSO cycle (figure 3) and on West Indonesia are mainly influenced by

IOD cycle (figure 4)


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Figure 1. Clustering of climate over Indonesian regions based on monthly rainfall


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Figure 2 Pontianak cycle

Pontianak Region

Correlation Sunspot vs Precip =0.88

0

50

100

150

200

1948

1951

1954

1957

1960

1963

1966

1969

1972

1975

1978

1981

1984

1987

1990

1993

1996

1999

2002

Years

Sunspot

Numbers

-50

0

50

100

150

200

Precipitation

ave-sunspot ave-precip


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Elnino-Lanina Years

0,00

50,00

100,00

150,00

200,00

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

Years

SunspotNumber

sspotLanina Elnino

Figure 3 ENSO cycle

Positive-Negative of Indian Dipole Mode Years

0,00

50,00

100,00

150,00

200,00

1948

1950

1952

1954

1956

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

Years

SunspotNumber

sspotNegative Positive

Figure 4 IOD cycle


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Influences of three cycles and Fuzzy Clustering

We use fuzzy c-means method with three seeding regions for initial matrix on Jayapura regions for East Indonesia, Bukittingi regions for

West Indonesia, and Pontianak regions for middle Indonesia. This method is based on fuzzy set as fuzzy c-partition of three cluster centers.

Fuzzy c-partition matrix for grouping a collection of n data set in to c classes, we define object function for fuzzy as Euclidian distance. Theresult in figure 5 shows that Jakarta region is similar 0.6 to solar cycle, below 0.5 to IOD cycle, and 0.5 to ENSO cycle.

Fuzzy c-means Algorithm

1. Fix c (2c n) and select a value for parameter m ,initialize the partition matrix U(0), each step in this algorithm will labeled r, wherer=0,1,2,..

2. Calculate the c centers {vi(r)} for each step.

3. Update the partition matrix forrth step, U (r) as follow:

=

=

=+

kIfor

mc

j dr

jk

dr

ikrik

1

)1'/(2

1 )(

)()1(

4. If + )()1( rr UU ,stop; otherwise set r=r+1 and return to step 2

5. Calculate the similarities of the c center: =

== n

k

mik

n

k kjxm

ik

ijv

1

'1

.'


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Figure 5 Fuzzy c-partition of three cluster center.


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Figure 6 Fuzzy c-partitions of four cluster centers

If we use fuzzy c-means method with four centers for initial matrix, then result that Jakarta region is similar 0.7 to solar cycle, 0.8 to IOD

cycle, and 0.8 to Surabaya climate region. From five centers or more it will not get better result.


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Conclusion

If solar activity average is used to cluster rainfall pattern on Indonesian regions then they influence monthly average rainfall, but if we have

to consider 11-year variation of solar activities to know extreme climate then clustering has to be based on yearly rainfall. The rainfall of middleIndonesia is strongly correlated with sunspot numbers, on East Indonesia the rainfall is influenced by ENSO cycle, and on West Indonesia the

rainfall is influenced by IOD cycle The result shows that for three centers, the rainfall cycle of Jakarta region is similar 0.6 to solar cycle, below

0.5 to IOD cycle, and 0.5 to ENSO cycle. The use of fuzzy c-means method with four centers get a result that rainfall cycle in Jakarta region is

similar 0.7 to solar cycle, 0.8 to IOD cycle, and 0.8 to rainfall cycle in Surabaya region.

Acknowledgement

This research is sponsored by RUT XI.3, KMNRT.

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Fuzzy Clustering and Extreme Weather/Climate in Indonesia