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Climate model
This document discusses climate modeling and weather modification in Indonesia. It outlines computing intensive climate models and introduces soft computing approaches like adaptive neuro-fuzzy inference systems (ANFIS) and fuzzy clustering for climate forecasting. It analyzes the relationship between sunspot numbers and rainfall in several Indonesian regions and finds correlations. The document concludes that numerical climate models need modifications for Indonesian regions and that solar activity is the main factor determining Indonesia's climate.
Climate model
- 1. Climate Model andWeather Modification in Indonesia The Houw Liong P.M. Siregar F.H. Widodo
- 2. Outline • Climate Model: Computing Intensive vs Soft Computing • Computing Intensive Climate Model • Soft Computing: ANFIS & Fuzzy Clustering • Fuzzy Rules & ANFIS • Fuzzy Clustering • Time Series of Sun Spot Numbers and Rainfall == Climate Forecasting • Weather Modification • Conclusion
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- 5. Climate Submodel • Cloudformation scheme :Arakawa, Kuo, .. • Biosphere response, carbon cycle, sulphur cycle,…. Cycle • Interactions : ocean, lands, cryosphere • Forcing : solar activities, volcano eruption, galactic cosmic rays
- 6. GCM & DARLAM •CO2 double in 100 years : • Temperature increase : 0.5 --- 1.5 in 50 years • Verification based on weather mainly on station at cities : urban warming instead of global warming. • Arakawa cloud formation scheme produced poor rainfall forecast : 0.46 for rainfall in Bandung • Sensitivity on initial and boundary conditions • Needs better data and better model • Forcing ??
- 7. Soft Computing :Adaptive Neuro Fuzzy Inference System A1 A2 B2 B1 N N ∏ ∏ layer1 layer2 layer3 layer4 layer5 x y 1w 2w 1w 2w x y
- 8. ANFIS • Layer 1 : • •x and y are input of ode -i and O1,i is membership function of fuzzy set A=(A1,A2) and B=(B1 ,B2 ) with membership function A is : • • • ai,bi, and ci are parameters • Layer 2 : output as the product of input membership functions : • 2 1, 1, ( ), 1, 2, ( ), 3,4, i i i A i B O x for i or O y for i µ µ − = = = = b2 i i A a cx 1 1 )x( − + =µ 2,1i)y()x(wO ii BA1i,2 =µµ==
- 9. • Layer 3in node -i : • • Layer 4 : Node -i is adaptive node with function node : 2,1i, ww w wO 21 i ii,3 = + == )ryqxp(wfwO iiiiiii,4 ++==
- 10. ANFIS • Layer 5: final output : • 5 i i i i i i i i w f O w f w = = ∑ ∑ ∑
- 11. • Fuzzy c-meansAlgorithm • Fix c (2≤c≤ n) and select a value for parameter m’, initialize the partition matrix U(0) , membership functions and the centers . Each step in this algorithm will labeled r, where r=0,1,2,.. • Repeat updating the partition matrix for rth step,U (r) until • ε≤−+ )()1( rr UU
- 12. • Calculate thenew membership functions 1 )1'/(2 1 )( )( )1( − − ∑ = =+ m c j d r jk d r ikr ik µ
- 13. • set r=r+1 •Calculate the new c centers : ∑ = ∑ == n k m ik n k kj xm ik ijv 1 ' 1 .' µ µ
- 14. Solar activities &Climate
- 15. Cosmic ray &Sunspot
- 16. SSN, AA Index& CME
- 17. Sunspot vs Rainfallin Jayapura Region 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
- 18. Sunspot vs Rainfallin Jayapura region Jaya Pura 0 50 100 150 200 250 300 350 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 Years CurahHujan Jayapuran (mm/bulan) 0 50 100 150 200 BilanganBintik Matahari Avg precip sspot
- 19. Sunspot vs Rainfallin Bukitinggi Region Kaitan Curah Hujan di Wilayah Bukittinggi dan Bintik Matahari 0 50 100 150 200 250 300 350 1 4 7 10 1316 19 22 25 28 31 34 37 40 43 46 49 52 55 CurahHujanBukittingi( mm/bulan) 0 50 100 150 200 BilanganBintik Matahari CH Bukittinggi Bilangan Bintik Matahari
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- 22. Positive-Negative of IndianDipole 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 sspot Negative Positive
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- 25. Concluding Remarks • NumericalClimate Model for Indonesian regions needs modifications • Long term climate prediction has poor accuracy due to chaos & forcing • ANFIS and Fuzzy Clustering can be used in forecasting of climate in Indonesia • Solar Activity is the main factor that determined climate in Indonesian regions