Lampiran 1. Albedo dari beberapa jenis permukaan. Tipe Observasi. Albedo (%) Permukaan. Pengamat

LAMPIRAN

Lampiran 1. Albedo dari beberapa jenis permukaan Permukaan Bay Bay and River Inland Waters Ocean Ocean, deep Ocean, near shore, solar elevation 47° Ocean, near shore, solar elevation 43° Ocean, near shore, solar elevation 20° Ocean, near shore, solar elevation 12° Ocean, near shore, solar elevation 51/2° Forest Green Forest Forest Forest, snow-covered ground Ground, bare Ground, bare, very white Ground, bare, some trees Ground, wet, 70-85% bare Ground, moist, 70-95% bare Black mold, dry Black mold, wet Sand, dry Desert, Mojave Desert, Death Valley Sand, wet Fields, dry plowed Fields, green Fields, green Fields, wheat Fields, unspecified Grass, dry Grass, high dry Grass, dry, no sun Grass, high fresh Grass, high wet Grass, wet no sun Grass, wet sun Snow, fresh Snow, several days old, white, smooth Snow, fresh (highest value) Snow, old (lowest value) Snow, white field Ice, sparse snow cover Clouds, stratus overcast, 0-500 feet thick Clouds, stratus overcast, 500-1000 feet thick

Tipe Observasi va va va va va tg tg tg tg tg va va va va va va va ta ta tg tg tg ta ta tg va va va va va va tg tg tg tg tg tg tg tg tg tg va ta ta ta

Albedo (%) 3 - 4 6 - 10 5 - 10 3 - 7 3 - 5 4 6 14 30 46 3 4 3 10 10 11 7 8 9 14 8 18 24 25 9 20 10 3 7 5 15 31 19 26 22 14 33 81 70 87 46 70 69 5 31

-

6 10 5 25 20

-

9 12

-

28

-

25 15 6 10 25 33 22

26 37 86

86 63 75

Pengamat KH TH L TH L A A A A A KH TH L KH L KH KH F F A A A M M A TH TH KH KH L TH A K A A K K A A K K KH M N N

Permukaan Clouds, stratus overcast, 1000-2000 feet thick Clouds, dense, opaque Clouds, dense, nearly opaque Clouds, thin Clouds, stratus, 600-1600 feet thick Clouds, stratocumulus overcast Clouds, altostratus, occasional breaks Clouds, altostratus overcast Clouds, cirrostratus and altostratus overcast Clouds, cirrostratus overcast

Tipe Observasi ta va va va ta ta ta ta ta ta

Albedo (%) 59 55 44 36 78 56 17 39 49 44

-

84 78 40 81 36 59 64 50

Pengamat N L L L Al F F F F F

Keterangan : Tipe Observasi : - v : pengukuran albedo dengan menggunakan photometer t : pengukuran albedo dengan menggunakan pyrheliometer, pyranometer - a : pengukurang dengan menggunakan aircraft (pesawat) - g : pengukuran dilakukan di permukaan (ground) Pengamat : - A - Al - B - D - F -

: Ångström, A. Geograf. Ann., vol.7, p.321, 1925 : Aldrich, L. B., Smithsonian Misc. Coll., vol.69, No.10, 1919 : Baur, F., and philips, H., Gerl. Beitr. Geophys., vol. 42, p.160. : Danjon, A., Ann. L’Obs. Strasbourg 3 No.3, p.193, 1936. : Fritz, S. Buil. Amel. Meteorol. Soc., vol. 29. p.303, 1948; vol.31, p.251, 1950; Journ. Meteorol., vol.58, p.59, 1930. K : Klitin, N. N., Month. Wheat. Rev., vol.58, p.59, 1930 KH : Kimball, H. H., and Hand, I. F., Month. Weath. Rev., vol.58, p.280, 1930 L : Luckiesh, M.Astrophys. Journ., vol.49, p.108, 1919. M : MacDonald, T. H., private communication, 1949. N : Neiburger, M., U. C. L. A., Dep. Of Meteorol. Papers in Meteorol., No.9, 1948; also Joun. Meteorol., vol.6, p.98, 1949. TH : Tousey, R., ad Hulburt, E. O., Journ. Opt. Soc. Amer., vol.37, p.28, 1947

Lampiran 2. Parameter Input dan Output dalam model REMO Parameter Input 129 Surface geopotential (orography) 172 Land sea mask 173 Surface roughness length 229 field capacity of soil 200 leaf area index 226 FAO data set (soil data flags) 212 Vegetation type 198 Vegetation ratio 174 Surface background albedo 199 Orographic variance (for runoff) 134 Surface pressure 130 Temperature 139 Surface temperature 206 Snow temperature 207 Soil temperature TD3 208 Soil temperature TD4 209 Soil temperature TD5 170 Deep soil temperature 183 Soil temperature 131 u-Velocity 132 v-Velocity 133 Specific humidity 153 Liquid water content 140 Soil wetness 232 Glacier mask 194 Skin reservoir content (t1) 141 Snow depth 156 Geopotential height ‘********************************** * Parameter Output 130 Temperature 131 u-velocity 132 v-velocity 133 Specific humidity 153 Liquid water content 134 Surface pressure 135 Vertical velocity 139 Surface temperature 140 Soil wetness 141 Snow depth 142 Large scale precipitation 143 Convective precipitation 144 Snow fall 145 Boundary layer dissipation 146 Surface sensible heat flux 147 Surface latent heat flux 159 ustar**3 160 Surface runoff 162 Cloud cover

163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 185 186 187 188 189 190 191 192 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213

Total cloud cover Total cloud cover 10m u-velocity 10m v-velocity 2m temperature 2m dew point temperature Surface temperature Deep soil temperature 10m windspeed Land sea mask Surface roughness length Surface background albedo Surface albedo Net surface solar radiation Net surface thermal radiation Net top solar radiation Top thermal radiation (OLR) Surface u-stress Surface v-stress Surface evaporation Soil temperature Net surf. solar radiation Net surf. thermal radiation Net top solar radiation Net top thermal radiation Surface solar cloud forcing Surface thermal cloud forcing Top solar cloud forcing Top thermal cloud forcing Skin reservoir content (t1) u-Gravity wave stress v-Gravity wave stress Gravity wave dissipation Vegetation ratio Orographic variance (for runoff) Leaf area index Maximum 2m-temperature Minimum 2m-temperature Top solar radiation upward Surface solar radiation upward Surface thermal radiation upward Snow temperature Soil temperature TD3 Soil temperature TD4 Soil temperature TD5 Sea ice cover Sea ice depth Vegetation type (effective) sea-ice skin temp

214 215 216 217 218 220 221 223 223 224 226

Maximum surface temperature Minimum surface temperature Maximum 10m-wind speed Maximum heig of conv cloud top Snow melt Residual surface heat budget Snow depth change Cloud cover Cloud cover Turbulent kinetic energy FAO data set (soil data flags)

227 228 229 230 231 232 129 156

Heat capacity of soil Soil diffusivity Field capacity of soil Vert-ly integ spec. humidity Vert-ly integ liq water cont Glacier mask Surface geopotential (orography) Geopotential height

Lampiran 3. Merubah Format data dari BIG endian menjadi LITTLE endian Untuk melakukan proses ini terlebih dahulu disiapkan data (BIG endian) directory .../xa dan buat directory …/xalin untuk menyimpan hasil proses (LITTLE endian). Dan pastikan file uswap ada pada directory ~/bin Kemudian buat Script berikut dan simpan dengan nama conv_b2l. #! /bin/bash # set -ex # # converts bigendian data to littleendian data # compile uswap in directory uread # INPUTDIR=/home/sofyan/remo/xa OUTPUTDIR=/home/sofyan/remo/xalin cd ${INPUTDIR} for I in * do # for ext4,remo etc uswap -x -i ${I} -o ${OUTPUTDIR}/${I} # for ext8 #uswap -x -d -i ${I} -o ${OUTPUTDIR}/${I} done exit

Kemudian script conv_b2l dieksekusi di konsule dengan menggunakan perintah eksekusi (./conv_b2l).

Lampiran 4. Script merubah rasio hutan Script ini dibuat dengan menggunakan bahasa basic dan dijalankan dengan menggunakan system operasi Microsoft Windows. Sebelum script ini dijalankan terlebih dahulu disiapkan data ekstraksi berupa data : o Parameter 172 : Land Sea Mask (sudah diedit : Pulau kalimantan bernilai 1 dan area lain bernilai 0) o Parameter 174 : Albedo o Parameter 198 : Rasio Vegetasi o Parameter 200 : LAI o Parameter 212 : Tipe Vegetasi o Parameter 229 : Kapasitas Lapang Dim Dim Dim Dim Dim Dim Dim Dim Dim Dim

Mask(101,55) as Single Albd(101,55) as Single RasV(101,55) as Single LAI(101,55) as Single TypeV(101,55) as Single KL(101,55) as Single i as Integer j as Integer TotalRasioIN as single TotalRasioOUT as single

‘(Parameter ‘(Parameter ‘(Parameter ‘(Parameter ‘(Parameter ‘(Parameter

172) 174) 198) 200) 212) 229)

‘Modul DataProses ‘ - Membaca File Input ‘ – Membuat File Output ‘ - Membaca Data Parameter ‘ – Menulis Data Olahan (Penurunan Rasio Vegetasi) Public Sub DataProses() ‘Membaca File Input Open “D:\Data\LSM.txt” For Input as #1 Open “D:\Data\Albedo.txt” For Input as #2 Open “D:\Data\RasioV.txt” For Input as #3 Open “D:\Data\LAI.txt” For Input as #4 Open “D:\Data\TipeV.txt” For Input as #5 Open “D:\Data\KL.txt” For Input as #6 ‘Output File Open “D:\Data\OutLSM.txt” For Output as #7 Open “D:\Data\OutAlbedo.txt” For Output as #8 Open “D:\Data\OutRasioV.txt” For Output as #9 Open “D:\Data\OutLAI.txt” For Output as #10 Open “D:\Data\OutTipeV.txt” For Output as #11 Open “D:\Data\OutKL.txt” For Output as #12 ‘Membaca Data i = 0 j = 1 While not EOF(1) If i = 101 then i = 0 j = j + 1 end if i = i + 1 Input #1, Mask(i,j)

Land Sea Mask Albedo Rasio Vegetasi Leaf Area Index Tipe Vegetasi Kapasitas Lapang

Input Input Input Input Input Wend

#2, #3, #4, #5, #6,

Albd(i,j) RasV(i,j) LAI(i,j) TypeV(i,j) KL(i,j)

‘ Nilai TotalRasio Awal For i = 1 to 101 For j = 1 to 55 TotalRasioIN = TotalRasioIN + (Mask(i,j)* RasV(i,j)) Next j Next i HitungUlang: Call PenurunanRasio TotalRasioOUT = 0 For i = 1 to 101 For j = 1 to 55 TotalRasioOUT = TotalRasioOUT + (Mask(i,j)* RasV(i,j)) Next j Next i ‘ Nilai TotalRasio Bergantung dari scenario yang diinginkan If TotalRasioOUT > TotalRasioIN – (TotalRasioIN * 25/100) Then GoTo HitungUlang End if ‘ – Menulis Data Olahan (Penurunan Rasio Vegetasi) Output #7, Mask(i,j) Output #8, Albd(i,j) Output #9, RasV(i,j) Output #10, LAI(i,j) Output #11, TypeV(i,j) Output #12, KL(i,j) ‘ Menutup File Close #1 : Close #2 : Close #3 : Close #4 : Close #5 : Close #6 Close #7 : Close #8 : Close #9 : Close #10 : Close #11 : Close #12 End sub ‘Modul Public Dim Dim

penurunan rasio hutan secara random sub PenurunanRasio() x as Integer y as Integer

RandomUlang: ‘Menentukan Pixel yang akan dirubah i = rnd() * 101 j = rnd() * 55 x = rnd() * 101 y = rnd() * 55 If i = 0 or j = 0 or x = 0 or y = 0 _ or i = x or j = y or Mask(I,J)=0 _ or Mask(x,y) = 0 Then GoTo RandomUlang

If RasV(i,j) < RasV(x,y) then GoTo RandomUlang ‘Menukar nilai parameter pada pixel yang sudah ditentukan Mask(i,j) = Mask(x,y) Albd(i,j) = Albd(x,y) RasV(i,j) = RasV(x,y) LAI(i,j) = LAI(x,y) TypeV(i,j) = TypeV(x,y) KL(i,j) = KL(x,y) End Sub

Lampiran 5. Script untuk menjalankan model REMO Untuk melakukan proses ini terlebih dahulu disiapkan data (LITTLE endian) pada directory xalin. Dan siapkan beberapa directory (xe,xf,xt) untuk menyimpan keluaran model Dan pastikan bahwa model iklim remo sudah ter-install pada PC yang digunakan. Kemudian buat Script berikut dan simpan dengan nama remo_ind_chain. #!/bin/bash # set -ex cd /tmp set +e mkdir dump cd dump rm -rf * set -ex # # PFL=/home/sofyan/remo/remo5.0_pc/libs PFL2=/home/sofyan/remo/jobs EXP=400 YEXP=\'${EXP}\' # # # Membaca jam awal dari jobs saat ini (RSA) # Membaca jam akhir dari bulan saat ini (REND) # RSA=`cat ${PFL2}/RSA` REND=`cat ${PFL2}/REND` # # Jika rantai selesai (mencapai REND) keluar # if [ ${RSA} -ge ${REND} ] then cd ${PFL2} time put_remo_results & exit fi # # Akhir dari job saat ini dihitung (48 jam setelah jam awal) # RSE=èxpr ${RSA} + 48` if [ ${RSE} -ge ${REND} ] then RSE=${REND} fi # membuat file parameter menjalankan REMO kedalam file namanya INPUT cat > INPUT << EOF &EMGRID PHILU=-19.0, RLALU=91.0, POLPHI=90.0, POLLAM=180.0, DLAM=0.5, DPHI=0.5, &END

&RUNCTL NHANF=$RSA, NHENDE=$RSE, YADAT='02019600', NHEAA=6, NHDEA=6, NHFORA=$RSE, NHDFOR=9999999, NHTAA=6, NHDTA=6, NHDAA=9999999, NHDDA=9999999, NHDMXN=6, DT=300.0, NHDR=6, LMOMON=.FALSE. &END &DYNCTL &END &PHYCTL HDRAD=1, LPHYEM=.FALSE., LAKKU=.FALSE., &END &NMICTL &END &PRICTL &END &DATEN YADEN='400', YRDEN='400', YEDEN='400', YFDEN='400', YTDEN='400', YADCAT='/home/sofyan/remo/xalin', YRDCAT='/home/sofyan/remo/xalin', YEDCAT='/home/sofyan/remo/xe', YFDCAT='/home/sofyan/remo/xf', YTDCAT='/home/sofyan/remo/xt', YTVARN='APRL ','APRC ','APRS ','ALWCVI ','QVI ', 'RUNOFF ','DRAIN ','SNMEL ','DSNAC ','EVAP ','SRADS ', 'TRADS ','SRAD0 ','TRAD0 ','AHFS ','AHFL ','ACLCV ', 'WSECH ','SN ','TEMP2 ','TSECH ','TD ','TDCL ', 'TSN ','TD3 ','TD4 ','TD5 ', &END EOF # Disini MENJALANKAN REMO ${PFL}/remo_101x55x20x1.exe < INPUT # Menyimpan data jam akhir job ini kedalam file, diberi nama RSA set +ex cat > fort.20 << EOC ${RSE} EOC mv fort.20 ${PFL2}/RSA # cd /home/sofyan/remo/xf # Menghapus xf_file lama (dua file) ANZ=`ls | wc -w` if [ ${ANZ} -eq 4 ] then

IND=1 for FILE in `ls -rt`; do if [ ${IND} -le 2 ]; then rm $FILE fi IND=èxpr ${IND} + 1` done fi # kembali ke direktori awal dan menjalankan rantai berikutnya cd ${PFL2} time remo_ind_chain & #time remo_ind_chain >> remo_out${RSA} & # exit

Kemudian script remo_ind_chain dieksekusi di konsule dengan menggunakan perintah eksekusi (./remo_ind_chain).

Lampiran 6. Script untuk mengekstrak model REMO Untuk melakukan proses ini terlebih dahulu disiapkan data keluaran model Dan pastikan bahwa model iklim remo sudah ter-install pada PC yang digunakan. ). Dan pastikan file pure4 dan yefis ada pada directory ~/bin Kemudian buat Script berikut dan simpan dengan nama script_all. #!/bin/bash # # This script extracting precipitation components (142+143) set -ex YY=96 MMM=01 ART='.tar' DATT=xt RUN=400 PARMA=142 PARMB=143 ERUN=e${RUN}xe NUMMERA=$PARMA,$PARMB #NUMMERA=182,139 # pindah ke direktori kerja cd /home/sofyan/remo/xeot # buat file parameter input untuk yefis (INPUTA) dan pure4 (INPUTB) # pertama check jika kedua file ada, jika ya hapus keduanya if [ -f INPUTA ];then rm INPUTA fi if [ -f INPUTB ];then rm INPUTB fi ################### cat > INPUTA << EOF &DATEN ICODE=${NUMMERA} IEXP=$RUN &END EOF # cat > INPUTB << EOF 101 55 2 91 91.5 2 -19 -18.5 EOF ################### # loop tahunan while [ ${YY} -le 96 ] do if [ ${MMM} -ne 0 ]; then MM=${MMM} MMM=0 else MM=01 MMM=0 fi

# loop bulanan while [ ${MM} -le 01 ] do INTE=1 # buat daftar panjang seluruh file berawal e400xt, proses satu satu for FILE in `ls ${ERUN}??${MM}${YY}??`; do NEWFILE=`basename $FILE` echo $NEWFILE # yefis mengubah format REMO output ke format ieee yefis < INPUTA ${FILE} ${NEWFILE}.ieee # pure4 mengubah format ieee ke format pure binary atau grads # sekaligus dibuat ctl file untuk grads pure4 grads ${NEWFILE}.ieee ${FILE}.grd < INPUTB >> ${FILE}.ctl # akumulasikan seluruh file hasil ke file hasil bulanan (data 6 jaman) cat ${FILE}.grd >> ${ERUN}${MM}${YY}.grd rm ${FILE} rm ${FILE}.ieee ${FILE}.grd # pindah ke indeks file berikutnya INTE=èxpr ${INTE} + 1` done # jangan lupa juga lakukan proses serupa untuk yang jam 00 bulan berikutnya yefis < INPUTA ${ERUN}01????00 dummy.ieee pure4 grads dummy.ieee dummy.grd < INPUTB >> dummy.ctl cat dummy.grd >> ${ERUN}${MM}${YY}.grd rm dummy.* ${ERUN}01????00 # mengenali nama bulan yang sedang di proses case ${MM} in 01) MON=jan;; 02) MON=feb;; 03) MON=mar;; 04) MON=apr;; 05) MON=may;; 06) MON=jun;; 07) MON=jul;; 08) MON=aug;; 09) MON=sep;; 10) MON=oct;; 11) MON=nov;; 12) MON=dec;; esac ##check jika ctl_file untuk bulan ini ada, jika tidak buatkan if [ ! -f ${ERUN}${MM}${YY}.ctl ] then ##### cat > ${ERUN}${MM}${YY}.ctl << EOF DSET ${ERUN}${MM}${YY}.grd UNDEF 9e+09 XDEF 101 LINEAR 91.000000 0.500000 YDEF 55 LINEAR -19.000000 0.500000 TDEF ${INTE} LINEAR 06:00Z1${MON}${YY} 06hr ZDEF 1 LINEAR 1000 -1 VARS 2 c$PARMA 1 0 CODE $PARMA c$PARMB 1 0 CODE $PARMB ENDVARS EOF #####

fi rm ${ERUN}??${MM}${YY}??.ctl # hitung bulan berikutnya if [ ${MM} -le 08 ]; then MM=0èxpr ${MM} + 1` else MM=èxpr ${MM} + 1` fi done # hitung tahun berikutnya YY=èxpr ${YY} + 1` done rm INPUTA INPUTB ####### exit

Kemudian script script_all dieksekusi di konsule dengan menggunakan perintah eksekusi (./script_all).

Lampiran 7. Uji Statistik dari unsur iklim 1.

CURAH HUJAN MUSIMAM Ranks N R-25 - KONTROL

R-50 - KONTROL

R-50 - R-25

718a 742b 0c 1460 661d 799e 0f 1460 678g 781h 1i 1460

Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total Negative Ranks Positive Ranks Ties Total

Mean Rank 728.24 732.68

Sum of Ranks 522878.50 543651.50

722.40 737.20

477505.50 589024.50

733.85 726.66

497548.50 567521.50

a. R-25 < KONTROL b. R-25 > KONTROL

Test Statisticsb

c. KONTROL = R-25 d. R-50 < KONTROL e. R-50 > KONTROL

R-25 KONTROL -.645a .519

Z Asymp. Sig. (2-tailed)

f. KONTROL = R-50 g. R-50 < R-25

a. Based on negative ranks.

h. R-50 > R-25

b. Wilcoxon Signed Ranks Test

R-50 R-50 - R-25 KONTROL -3.461a -2.174a .001 .030

i. R-25 = R-50

2.

CURAH HUJAN KONVEKTIF Ranks N R 25 - Kontrol

R 50 - Kontrol

R 50 - R 25


570a 887b 3c 1460 562d 897e 1f 1460 728g 732h 0i 1460

Mean Rank 693.61 751.74

Sum of Ranks 395359.00 666794.00

689.43 755.42

387458.50 677611.50

731.55 729.45

532570.00 533960.00

a. R 25 < Kontrol

Test Statisticsb

b. R 25 > Kontrol c. Kontrol = R 25 d. R 50 < Kontrol e. R 50 > Kontrol f. Kontrol = R 50 g. R 50 < R 25 h. R 50 > R 25 i. R 25 = R 50


R 25 - Kontrol R 50 - Kontrol R 50 - R 25 -8.449a -9.013a -.043a .000 .000 .966

a. Based on negative ranks. b. Wilcoxon Signed Ranks Test

3.

EVAPORASI Ranks N R 25 - Kontrol


R 50 - Kontrol

R 50 - R 25

486a 973b 1c 1460 583d 876e 1f 1460 879g 580h 1i 1460

Mean Rank 799.77 695.15

Sum of Ranks 388687.50 676382.50

831.97 662.14

485037.50 580032.50

745.71 706.19

655480.50 409589.50

a. R 25 < Kontrol b. R 25 > Kontrol

Test Statisticsc

c. Kontrol = R 25 d. R 50 < Kontrol e. R 50 > Kontrol


f. Kontrol = R 50

R 25 - Kontrol R 50 - Kontrol R 50 - R 25 -8.937a -2.951a -7.638b .000 .003 .000


g. R 50 < R 25

b. Based on positive ranks. c. Wilcoxon Signed Ranks Test

h. R 50 > R 25 i. R 25 = R 50

4.

LIMPASAN Ranks N R25 - Kontrol

R50 - Kontrol

R50 - R25


472a 752b 236c 1460 551d 908e 1f 1460 709g 750h 1i 1460

Mean Rank 582.10 631.58

Sum of Ranks 274753.50 474946.50

700.47 747.92

385959.00 679111.00

720.10 739.36

510549.00 554521.00

a. R25 < Kontrol

Test Statisticsb

b. R25 > Kontrol c. Kontrol = R25 d. R50 < Kontrol e. R50 > Kontrol


R25 - Kontrol R50 - Kontrol R50 - R25 -8.092a -9.106a -1.366a .000 .000 .172

f. Kontrol = R50


g. R50 < R25

b. Wilcoxon Signed Ranks Test

h. R50 > R25 i. R25 = R50

5.

SUHU UDARA Ranks N R25 - Kontrol Negative Ranks Positive Ranks Ties Total R50 - Kontrol Negative Ranks Positive Ranks Ties Total R50 - R25 Negative Ranks Positive Ranks Ties Total a. R25 < Kontrol b. R25 > Kontrol c. Kontrol = R25 d. R50 < Kontrol e. R50 > Kontrol f. Kontrol = R50 g. R50 < R25 h. R50 > R25 i. R25 = R50

404a 1052b 4c 1460 289d 1171e 0f 1460 513g 945h 2i 1460

Mean Rank Sum of Ranks 707.98 286023.50 736.38 774672.50

693.15 739.72

200319.00 866211.00

635.50 780.53

326010.00 737601.00

Test Statisticsb R25 - Kontrol R50 - Kontrol R50 - R25 Z -15.226a -20.664a -12.799a Asymp. Sig. (2-tailed) .000 .000 .000 a. Based on negative ranks. b. Wilcoxon Signed Ranks Test

Lampiran 8. Data curah hujan di beberapa stasiun di pulau Kalimantan pada tahun 1996

No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Nama Stasiun Singkawang Pontianak Anjungan Kembayan Paloh Siantan Susilo Sintang Nanga Pinoh Banjarmasin Banjarbaru Stajer Palangkaraya Muara Tewah Balikpapan Tarakan Samarinda Tanjung Redup Tanjung Selor Long Bawang

Jan 465 249 294 245 563 345 438 423 415 414 336 256 256 304 315 372 323 292

Feb 350 363 357 273 355 353 321 328 339 340 326 405 386 392 275 385 377 304 113

Mar 214 344 320 319 156 289 362 354 350 349 253 240 419 448 300 376 366 183

Apr 231 354 335 229 148 308 382 363 277 277 321 186 230 439 203 278 232 262

May 112 159 153 73 78 143 166 166 220 220 181 165 249 330 304 262 216

Jun 340 330 330 359 351 78 335 331 249 250 440 266 293 287 339 278 316 239

Jul 191 204 204 202 171 351 315 345 196 197 607 171 111 192 248 129 195 183 -

Aug 327 340 338 290 320 171 350 351 440 439 373 366 378 402 319 211 284 -

Sep 282 183 207 266 308 320 432 403 113 114 238 246 218 442 209 107 -

Oct 525 639 611 525 488 308 485 477 358 357 308 189 341 304 294 -

Nov 263 276 278 443 242 488 306 313 324 324 130 391 568 378 329 224 251 -

Dec 277 235 244 351 295 242 276 278 491 489 280 244 309 579 332 275 -

Total 3577 3676 3671 3575 3475 3396 4168 4132 3772 3770 3793 3125 3758 4184 3407 3197 -

Lampiran 1. Albedo dari beberapa jenis permukaan. Tipe Observasi. Albedo (%) Permukaan. Pengamat

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