DAFTAR PUSTAKA Bergant K, Kajfez-Bogataj L, Crepinsek Z. 2002. Downscaling of generalcirculation- model-simulated average monthly air temperature to the beginning of flowering of dandelion (Taraxacum officinale) in Slovenia. Int J Biometeorol, 46:22-32. Biau G, Zorita E, Storch H von, Wacker-Nagel H. 1999. Estimation of precipitation by kriging in the EOF space of the sea level pressure field. J Clim, 12:1070-1085. BIOCLIM. 2004. Application of statistical downscaling within the BIOCLIM hierarchical strategy: methods, data requirements, and underlying assumptions. Technical Note D6b. Http://www.andra.fr/bioclim/pdf/d6b.pdf [25 April 2005]. BMG [Badan Meteorologi dan Geofisika]. 2003. Pengkajian prakiraan musim untuk pertanian di kabupaten Indramayu. Laporan Akhir. Badan Meteorologi dan Geofisika, Jakarta. Bocchieri JR, Glahn HR. 1972. Use of model output statistics for predicting ceiling height. Monthly Weather Review, 100(12):869-877. Busuioc A, Chen D, Hellstrom C. 2001. Perfo rmance of statistical downscaling models in GCM validation and regional climate change estimates: Application for Swedish precipitation. Int J Climatol, 21:557-578. Cavazos T. 1999. Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in Northeastern Mexico and Southeastern Texas. J Clim, 12:1506-1523. Cavazos T, Hewitson B. 2002. Rela tive performance of empirical predictors of daily precipitation. Proc. of the 1st Biennial Meeting of the IEMSS, Lugano, Switzerland, vol. 2:349-354. Charles SP, Bates BC, Whetton PH, Hughes JP. 1999a. Validation of downscaling models for changed climate conditions: Case study of southwestern Australia. Clim Res, 12:1-14. Charles SP, Bates BC, Hughes JP. 1999b. A spasiotemporal model for downscaling precipitation occurences and amounts. J Geophys Res., Vol 104, no. D24: 31657-31667. Chen D, Chen Y. 2002. Association between winter temperature in China and upper air circulation over East Asia revealed by canonical correlation analysis. Global and Planetary Change, 780:1-11. Http://www.elsevier.com/locate/glopacha. [22 Maret 2003]. Dawson CW, Wilby RL. 2001. Hydrological modeling using artificial neural networks. Progress in Physical Geography, 25(1): 80-108. Donoho DL, Johnstone IM. 1989. Projection-based approximation and a duality with kernel methods. Ann Statist 17:58-106. Fenoglia-Marc L. 2001. Analysis and representation of regional sea- level variability from altimetry and atmospheric oceanic data. J Int Geophys, 145:1-18. Fernandez E. 2005. On the influence of predictors area in statistical downscaling of daily parameters. Report no.09/2005. Norwegian Meteorological Institute, Oslo.
Friedman JH, Stuetzle W. 1981. Projection pursuit regression. J Amer Statist Assoc, 376:817-823. Fuentes U, Heimann D. 2000. An improved statistical-dynamical downscaling scheme and its application to the Alpine precipitation climatology. Theoretical and Applied Climatology, 65:119-135. GFM (Departemen Geofisika dan Meteorologi, FMIPA IPB) & BMG (Badan Meteorologi dan Geofisika). 2003. Climate Forecast Information Application: Case Study at Indramayu District. Final Report on Second Phase Extreme Climate Events Project. Giorgi F, Hewitson H. 2001. Regional climate information. Evaluation and Projections. http://www.grida.no/climate/ipcc_tar/wg1/pdf/TAR-10.PDF [10 Januari 2006] Halls P. 1989. On projection pursuit regression. Ann Statist, 12(2):573-588. Hastie TJ, Tibshirani RJ. 1990. Generalized Additive Models. Chapman & Hall/CRC. London. Hewitson B. 1994. Regional climate in the GISS General Circulation Model surface air temperature. J Clim, 7:283-303. Huber PJ. 1985. Projection pursuit. Ann Statist, 12(2):435-475. Huth R, Kysely J. 2000. Constructing site-specific climate change scenarios on a monthly scale using statistical downscaling. Theoretical and Applied Climatology, 66:13-27. Jimenez LO, Landgrebe DA. 1999. Hyperspectral data analysis and supervised feature reduction via projection pursuit. IEEE Trans. On Geoscience and Remote Sensing, 37:2653-2667. Johnny C, Chan L, Shi Jiu-En. 1997. Application of projection-pursuit principal component analysis method to climate studies. Int J Climatol, 17:103-113. Jones MC, Sibson R. 1987. What is projection pursuit? J R Statist Soc A, 150:136. Landman WA, Tennant WJ. 2000. Statistical downscaling of monthly forecasts. Int J Climatol, 20:1521-1532. Lanza LG, Ramirez JA, Todini E. 2001. Stochastic rainfall interpolation and downscaling. Hydrology and Earth System Sciences, 5(2):139-143. Li X, Sailor D. 2000. Application of tree-structured regression for regional precipitation prediction using general circulation model output. Clim Res, 16:17-30. Malthouse EC. 1995. Nonlinear Partial Least Square. Dissertation, Field of Statistics. Northwestern University. Mole K, Boer R, Pawitan H, Ratag MA. 2001. Metode pengujian model- model sirkulasi umum untuk wilayah Indonesia. Prosiding Temu Ilmiah Prediksi Cuaca dan Iklim Nasional. Lembaga Penerbangan dan Antariksa Nasional (LAPAN), Bandung. Mpelasoka FS, Mullan AB, Heerdegen RG. 2001. New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches. Int J Climatol, 21:1415-1433. Notodiputro KA, Wigena AH, Fitriadi. 2004. Pendekatan regresi komponen utama dan ARIMA dalam statistical downscaling. Forum Statistika dan Komputasi. Edisi Khusus Seminar Nasional Statistika.
85
Nuryadi. 2005. Pemanfaatan informasi iklim untuk tingkat kabupaten. Contoh Indramayu dan Malang. Buku Panduan Seminar Sehari, Hari Meteorolgi Dunia ke 55, 25 Maret 2005. Jakarta. Posse C. 1995. Projection pursuit exploratory data analysis. Comput. Stat. & Data Analysis, 20:669-687. Ratag MA. 2001. Reanalisis curah hujan di Indonesia hasil simulasi model area terbatas resolusi tinggi CSIRO DARLAM. Prosiding Temu Ilmiah Prediksi Cuaca dan Iklim Nasional. Lembaga Penerbangan dan Antariksa Nasional (LAPAN), Bandung. Ryan TP. 1997. Modern Regression Methods. John Wiley & Sons, Inc. New York. Sailor DJ, Li X. 1999. A semiempirical downscaling approach for predicting regional temperature impact associated with climate change. J Clim, 12:103-114. Sailor DJ, Hu T, Li X, Rosen JN. 2000. A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change. Renewable Energy, 19:359-378. Schubert S, Henderson-Sellers A. 1997. A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in The Australian region. Climate Dynamics, 13:223-234. Scott DW. 1992. Multivariate Density Estimation. Theory, Practice and Visualization. John Wiley & Sons, Inc. New York. Simonoff JS. 1996. Smoothing Methods in Statistics. Springer. New York. Siswanto B, Ratag MA. 2001. Prediksi curah hujan dan temperatur permukaan bulanan berbasis model sirkulasi global GCM CSIRO-9. Prosiding Temu Ilmiah Prediksi Cuaca dan Iklim Nasional. Lembaga Penerbangan dan Antariksa Nasional (LAPAN), Bandung. Storch H von, Hewitson B, Mearns L. 2001. Review of empirical downscaling techniques. Http://www.nilu.no/regclim/~/presentation02.htm. [15 Februari 2004]. Storch H von, Zwiers FW. 1999. Statistical Analysis in Climate Research. Cambridge University Press. London. Trigo RM, Palutikof JP. 2001. Precipitation scenarios over Iberia: A comparison between direct GCM output and different downscaling techniques. J Clim, 14:4422-4446. Uvo CB et al. 2001. Statistical atmospheric downscaling for rainfall estimations in Kyushu Island Japan. Hydrology & Earth System Sciences, 5(2):259-271. Wigena AH, Aunuddin. 2004a. Aplikasi projection pursuit dalam pemodelan statistical downscaling. Forum Statistika dan Komputasi. Edisi Khusus Seminar Nasional Statistika. -----------------------------. 2004b. Aplikasi projection pursuit dan jaringan syaraf tiruan dalam pemodelan statistical downscaling. Statistika. Forum Teori dan Aplikasi Statistika, 4(2):7-10. Wigena AH, Syafitri UD, Millafanti YA. 2005. Penentuan domain dengan teknik variogram. Forum Statistika dan Komputasi (dalam proses penerbitan). Wilby RL, Wigley TML. 1997. Downscaling general circulation model output: A review of methods and limitations. Progress in Physical Geography, 21,4:530-548.
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------------------------------. 2000. Precipitation predictors for downscaling: Observed and general circulation model relationships. Int J Climatol, 20:641-661. Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS. 1998. Statistical downscaling of general circulation model output: A comparison of methods. Water Resources Research, 34(11): 2995-3008. Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO. 2004. Guidelines for use of climate scenarios developed from statistical downscaling. Http://ipcc-ddc.cru.uea.ac.uk/guidelines/StatDown_Guide.pdf [15 Mei 2005]. Wilks DS. 1995. Statistical Methods in the Atmospheric Sciences. Academic Press. New York. Xia X, An HZ. 1999. Projection pursuit autoregression in time series. J Time Series Anal, 20(6):693-714. Yarnal B, Comrie AC, Frakes B, Brown DP. 2001. Developments and prospects in synoptic climatology. Review. Int J Climatol, 21:1923-1950. Zorita E, Hughes JP, Lettenmaier DP, Storch H von. 1995. Stochastic characterization of regional circulation patterns for climate model diagnosis and estimation of local precipitation. J Clim, 8:1023-1042. Zorita E, Storch H von. 1999. The analog method as a simple statistical downscaling technique: Comparison with more complicated methods. J Clim, 12:2474-2489.
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LAMPIRAN
Lampiran 1. Daftar Istilah ARIMA ANN CART CCA CCC CSIRO DARLAM DPM DPM_BMG DPM_PPR GCM GFDL GISS LAM MARS MOS NCAR NCEP NHMM NWP NWS PCA PCR PP PPR RCM RMSEP TSR UKMO UKTR SD SLP SST STD SVD
Autoregressive Integrate Moving Average Artificial Neural Network Classification and Regression Tree Canonical Correlation Analysis Canadian Climate Center Commonwealth Scientific and Industrial Research Organization Division of Atmospheric Research Limited Area Model Daerah Prakiraan Musim DPM hasil pewilayahan olen BMG (2003) DPM hasil pewilayahan dengan model PPR General Circulation Model Geophysical Fluid Dynamic Laboratory Goddard Institute for Space Studies Limited Area Model Multivariate Additive Regression Spline Model Output Statistics National Centre for Atmospheric Research National Centers for Environmental Prediction Non Homogenuous Hidden Markov Model Numerical Weather Prediction National Weather Services Principal Component Analysis Principal Component Regression Projection Pursuit Projection Pursuit Regression Regional Circulation Model Root Mean Square Error of Prediction Tree Structure Regression United Kingdom Meteorological Office United Kingdom Meteorological Transient Statistical Downscaling Sea Level Pressure Sea Surface Temperature Standard Deviation Single Value Decomposition
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Lampiran 2. Elevasi dan Koordinat Setiap Stasiun di Kabupaten Indramayu No Stasiun 1a 10 10a 1 1c 4 11 2a 17 23a 7 12 16 15 9a 23b 5 13b 23 8 13a 29 18b 6 26 27 9 3b 14 3c 14b
Nama Stasiun Bugel Indramayu Cidempet Anjatan Tulangkacang Bulak/Kandanghaur Bangkir Bugis Karangasem Sudimampir Losarang Lohbener Wanguk Luwungsemut Tugu Juntinyuat Gabuswetan Jatibarang Ujungaris Cikedung Sudikampiran Temiyang Cipancuh Kroya Krangkeng Kedokan Bunder Sumurwatu Gantar Sukadana Bantarhuni Bondan
Elevasi(mdpl) 1 6 7 1 1 2 11 7 24 4 2 11 1 8 * 5 8 3 12 * 7 26 8 48 5 7 * 22 18 35 9
90
LS 6,299 6,345 6,352 6,355 6,357 6,363 6,385 6,389 6,395 6,402 6,405 6,406 6,416 6,427 6,433 6,433 6,445 6,456 6,457 6,467 6,482 6,487 6,488 6,489 6,503 6,509 6,517 6,528 6,546 6,589 6,606
BT 107,985 108,322 108,247 107,954 107,006 108,113 108,291 107,932 107,054 108,366 108,149 108,282 107,957 107,009 108,333 108,438 107,039 108,307 108,287 108,167 108,364 107,021 107,944 107,064 108,483 108,424 108,1 107,973 108,315 107,951 108,299
Lampiran 3. Dendrogram Pengelompokan Stasiun Curah Hujan
Similarity 31.50
54.33
77.17
100.00 t k el y kr a k ir ng ner lak r is ar a mp yu en un mp na ng ugu t an gis an em ya u t ng at uh an r hu tar ng n w g a k t pe u g a d m a a m ng B u r am S u an s ar hb e B u jga at ib ik a nti ng knb i m a ada edu T nj a B u we gas K r o s e kac ur anc on nta Ganm i y d u k k A B o o s r d d d U J ra d e Wa i Lw T l S um Ci p B B a L L In S u J K K e S u S u Ci C Te Gb K
Stasiun
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Lampiran 4. Curah Hujan (mm) Aktual dan Dugaan Setiap DPM_PPR DPM2_PPR
DPM1_PPR 500
500
400
400 300
Aktual
300
Aktual
200
Dugaan
200
Dugaan
100
100
0
0 1
2
3
4
5
6
7
8
9
10
1
11 12
2
3
4
5
DPM3_PPR
6
7
8
9
10
11 12
DPM4_PPR
500
500
400
400
300
Aktual
300
Aktual
200
Dugaan
200
Dugaan
100
100
0
0 1
2
3
4
5
6
7
8
9
10
11 12
1
DPM5_PPR 500 400 300
Aktual
200
Dugaan
100 0 1
2
3
4
5
6
7
8
9
10
11 12
92
2
3
4
5
6
7
8
9
10
11 12
Lampiran 5. Curah Hujan (mm) Aktual dan Dugaan Setiap DPM_BMG DPM1_BMG
DPM2_BMG
500
500
400
400
300
Aktual
300
Aktual
200
Dugaan
200
Dugaan
100
100
0
0 1
2
3
4
5
6
7
8
9
10 11 12
1
2
3
4
5
DPM3_BMG
6
7
8
9
10 11 12
DPM4_BMG
500
500
400
400
300
Aktual
300
Aktual
200
Dugaan
200
Dugaan
100
100
0
0 1
2
3
4
5
6
7
8
9
10
11 12
1
2
3
4
5
6
7
8
9
10 11 12
DPM6_BMG
DPM5_BMG 500
500
400
400
300
Aktual
300
Aktual
200
Dugaan
200
Dugaan
100
100
0
0 1
2
3
4
5
6
7
8
9
10
11 12
1
93
2
3
4
5
6
7
8
9
10 11 12
Lampiran 6. Sub-Program S-Plus yang digunakan untuk Pendugaan Model PPR ppreg(x, y, min.term, max.term=min.term, wt=rep(1,nrow(x)), rwt=rep(1,ncol(y)), xpred=NULL,optlevel=2,bass=0,span="cv") Outputs by ppreg: ypred matrix of predicted values for y given the matrix xpred. If xpred was not input, then ypred contains the residuals for the model fit.
fl2 the sum of squared residuals divided by the total corrected sums of squares.
alpha a minterm by ncol(x) matrix of the direction vectors, alpha[m,j] contains the j-th component of the direction in the m-th term.
beta a minterm by ncol(y) matrix of term weights, beta[m,k] contains the value of the term weight for the m-th term and the k-th response variable.
z a matrix of values to be plotted against zhat. z[i,m] contains the z value of the i-th observation in the m-th model term, i.e., z equals x %*% t(alpha). The columns of z have been sorted.
zhat a matrix of function values to be plotted. zhat[i,m] is the smoothed ordinate value (phi) of the i-th observation in the m-th model term evaluated at z[i,m].
allalpha a three dimensional array, the [m,j,M] element contains the j-th component of the direction in the m-th model term for the solution consisting of M terms. Values are zero for M less than minterm.
allbeta a three dimensional array, the [m,k,M] element contains the term weight for the m-th term and the k-th response variable for the solution consisting of M terms. Values are zero for M less than minterm.
esq esq[M] contains the fraction of unexplained variance for the solution consisting of M terms. Values are zero for M less than minterm.
esqrsp matrix that is ncol(y) by maxterm containing the fraction of unexplained variance for each response. esqrsp[k,M] is for the k-th response variable for the solution consisting of M terms, for M ranging from min.term to max.term. Other columns are zero.
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