Training Workshop on Environment Statistics Damascus, -8 April 00 Applied on Precipitation and Temperature data (Barada and Awaj Basin) Mazen Abou Abdallah it_div@acsad.org - ACSAD,Syria April 00, Damascus From -8 April. Principal Component Analysis (PCA) Empirical Orthogonal Function (EOF) TURKEY SYRIA LEBANON LEBANON DAMASCUS JORDAN Locations of climatic stations JORDAN ٢ ١
Principal Component Analysis (PCA) Precipitation The climate of the basin is classified as Mediterranean. It is characterized by wet cool winter and long hot dry summer. The principal meteorological factors which produce precipitation are cyclonic disturbances forming over the Mediterranean sea, or Atlantic ocean. In summer the subtropical region of the Azores anticyclone, establishes itself over the coastal area of the Mediterranean, thereby preventing the penetration of the cyclones from the sea into the Eastern Mediterranean region. In spring a low pressure area is formed resulting in the movement of the Khamasin wind system into Syria and adjacent areas. Precipitation varies over the Barada river basin from about 000 mm. in the Zebdani highlands to about 00 mm in the eastern lowlands. It occur mainly from October to May. The period from December to February accounts for 5 to 65% of the annual precipitation. virtually no rain falls from June to September. The total amount of precipitation on the basin is 67 mm or 566 MCM (Million Cubic Meters). some 8 mm or 0 MCM on the average, fall on the Damascus plain (Selkhoprom export, 87). ٣ Principal Component Analysis (PCA) Precipitation data between 6 76,For 8 Stations sn Year month 6,5,5,6,,6 6,5 6 60 0, 7,8,5, 6, 7 6 5, 5, 8,6 85,6, 07,5 6 76,7 67,,5,6 08,5 6, 77,5 5 6 7,7,,5 8,, 7, 6 6,5, 0, 0,5, 5,, 7 6, 6, 50, 0 8, 7,8 0,,8 8 6 0,, 8,6,5, 5,, 5, 65 76,7 0,, 0,5, 6,5 6,7 0 0 65 6 5,7 5,,5,6,6,8 65,5 8, 6,8 7,8 8,, 6,8 65 0,6 7,7 6 7, 6, 6,6 7,5 7 76 6, 7,6, 07 5, 0,7 5, 68 80 76 7,5, 8,, 6,, 7,7 8 77 8,8, 7, 5 7, 8,7,6 00, 8 8 8 77 77 77, 5,,,7 8,6,6,7,5,5, 0,7 8, 5,7 0,, 8,7,6,7 6,6,6 66,5 6, ٤ ٢
Principal Component Analysis (PCA) Precipitation Chart of Station no.: Annual Precipitation ( mlm ) 80 60 0 0 00 80 60 0 0 0 6-6 6-65 65-66 66-67 67-68 68-6 6-70 Date 70-7 7-7 7-7 7-7 7-75 75-76 76-77 ٥ Principal Component Analysis (PCA) Precipitation Chart of Station no.: 000 00 Annual Precipitation ( mlm ) 800 700 600 500 00 00 00 00 0 6-6 6-65 65-66 66-67 67-68 68-6 6-70 Date 70-7 7-7 7-7 7-7 7-75 75-76 76-77 ٦ ٣
Principal Component Analysis (PCA) The Eigenvectors for all modes and the percentage of the total variance in the observations, using PCA Station no X Y Level 67.8 mod.5 mod 7.7 mod.7 mod.7 mod5. mod6. mod7 0. mod8 06000 76000-0.875 0.558 0.08 0.76706 0.067-0.07 0.05-0.000 0500 707500 7-0. -0.7 0. -0.065 0.7 0.0575-0.705-0.7 8800 70550 6-0.860-0.0885 0.6-0.50 0.6-0.70 0.78 0.770 600 70750 80-0.06-0.8 0.0885 0.0857 0.787 0.8-0.7 0.86 88000-0.77 0.078 0.676-0.7-0.6705 0.758 0. -0.007 00 700 00-0.5-0.78-0.668 0.055-0.8600-0.6-0.76 0.058 500 7000-0.7 0.67-0.00-0.5 0.00 0.588 0.058 0.08 8000 7000 60-0.5-0.7-0.5 0.80 0. 0.506 0.6-0.00 5 modes 6.8 % ٧ Principal Component Analysis (PCA) 70000 60 80 7 6 80000 60000 0000 0000 0000 80000 00 00000 0000 0000 60000 80000 The Distribution of topographic levels ٨ ٤
Principal Component Analysis (PCA) -0.8 70000 60-0. -0. 7 80-0. -0.0 80000 6-0. -0.8 Eigenvector 60000 0000 0000 00 0000 80000-0. 00000 0000 0000 60000 80000 The Eigenvectors For mode var explained 67.8 ٩ Principal Component Analysis (PCA) 0.56 70000 60-0. 0.6 7 80-0. -0. 80000 6-0.05 0.08 Eigenvector 60000 0000 0000 00 0000 80000-0. 00000 0000 0000 60000 80000 The Eigenvectors For mode var explained.5 ١٠ ٥
Principal Component Analysis (PCA) 0.77 70000 60 0.8-0.5 7 80-0.07 0.0 6-0. -0.7 Eigenvector 80000 60000 0000 0000 00 0000 80000 0.05 00000 0000 0000 60000 80000 The Eigenvectors For mode var explained.7 ١١ Rotated Principal Component Analysis (PCA) The Eigenvectors for all modes and the percentage of the total variance in the observations, using Rotated PCA Station no X Y Level mod 7 mod mod 06000 76000-0.68-0.087 0.80 0500 707500 7-0.77-0.5 0.508 8800 70550 6-0.876-0.8755 0.5 600 70750 80-0.770-0.5666 0.56 88000-0.786-0.5 0.707 00 700 00-0. -0.8576 0.86 500 7000-0.88-0.55 0.78 8000 7000 60-0. -0.856-0.085 modes 0 % ١٢ ٦
٧ ١٣ Rotated Principal Component Analysis (PCA) 7 6 80 00 60-0. -0.80-0.87-0.77-0.80-0.6-0. -0.50 Rotated PCA - The Eigenvectors For mode var explained Eigenvector 0000 80000 00000 0000 0000 60000 80000 0000 0000 60000 80000 70000 ١٤ Extended Principal Component Analysis (PCA) -0.57 0.0 0.656 0. 0.5 0.057-0.066 60 7000 8000-0.57-0.606-0.00-0.58 0.8 0.08-0.5 7000 500-0.7 0.775 0.88 0.078 0.80 0.007-0.6706 00 700 00 0.0 0.0708-0.065-0.00 0.567 0.07-0.756 88000 0.0670 0.0680 0.00 0.67 0.085 0.066-0.0 80 70750 600 0.70 0.00-0.08 0.068 0.57 0.007-0.7 6 70550 8800 0.085 0.00 0.0056 0.0565 0.76 0.055-0.7 7 707500 0500 0.077-0.0806-0.770-0.0 0.70 0.0768-0.5 76000 06000 0.08-0.00705 0.0-0.05060-0.0675 0. 0.075 60 7000 8000-0.005 0.7-0.570-0.08 0.06 0.5078 0.007 7000 500 0.78 0.07 0.077-0.850-0.068 0. 0.007 00 700 00-0.067-0.055-0.078 0.07 0.00660 0.565 0. 88000 0.05-0.0 0.6-0.056-0.00558 0.88 0.086 80 70750 600-0.08-0.0700 0.005-0.00657 0.086 0.7 0.0 6 70550 8800-0.006-0.075 0.05-0.0 0.00706 0.5 0.05 7 707500 0500-0.00 0.50-0.78 0.08876 0.07 0.8565 0.0 76000 06000 0.05 0.5567 0.080-0.557-0.56 0.0-0.586 60 7000 8000 0.7-0.00-0.08 0.7556-0.767 0.08-0.70 7000 500 0.570 0.65-0.0-0.050-0. -0.0066-0.088 00 700 00-0.68-0. 0.005 0.05-0.8586 0.077-0.08 88000-0.0 0.6786 0.08-0.5-0.7 0.0600-0.86 80 70750 600-0.87-0.067 0.0075-0.07-0.775 0.06-0.5 6 70550 8800-0.0860 0.06768 0.0088-0.060-0.6868 0.06866-0.00 7 707500 0500 0.06-0.0-0.007 0.580-0. 0.078-0.86 76000 06000 mod7 mod6 mod5 mod mod mod mod.6.7 5.0 6.8.6.8.6 Level Y X Station no
Extended Principal Component Analysis (PCA) Nov -0. 70000 60-0.5-0.7 7-0.7 80-0.7 6-0.8-0. 80000 60000 0000 Dec 0000 00 0000 80000-0.6 00000 0000 0000 60000 80000 0.0 70000 0.0 60-0.07 Jan 70000 60 0.5 0.7 0. Mode Var Exp..6 7 80 0.0-0.0 6 0.0 0.0 7 80 0.0 0. 6 0.6 0.6 80000 80000 60000 60000 0000 0000 00 0000 80000-0.07 00000 0000 0000 60000 80000 0000 0000 00 0000 80000 0. 00000 0000 0000 60000 80000 ١٥ Principal Component Analysis (PCA) The Eigenvectors for all modes and the percentage of the total variance in the observations, using PCA Station no X Y Level 67.8 mod.5 mod 7.7 mod.7 mod.7 mod5. mod6. mod7 0. mod8 06000 76000-0.875 0.558 0.08 0.76706 0.067-0.07 0.05-0.000 0500 707500 7-0. -0.7 0. -0.065 0.7 0.0575-0.705-0.7 8800 70550 6-0.860-0.0885 0.6-0.50 0.6-0.70 0.78 0.770 600 70750 80-0.06-0.8 0.0885 0.0857 0.787 0.8-0.7 0.86 88000-0.77 0.078 0.676-0.7-0.6705 0.758 0. -0.007 00 700 00-0.5-0.78-0.668 0.055-0.8600-0.6-0.76 0.058 500 7000-0.7 0.67-0.00-0.5 0.00 0.588 0.058 0.08 8000 7000 60-0.5-0.7-0.5 0.80 0. 0.506 0.6-0.00 5 modes 6.8 % ١٦ ٨
٩ ١٧ Canonical Correlation Analysis (CCA) NOAA NCDC GCPC ١٨ Temperature of 6 station between 6-77,5,5 0,0, 5,7 6, 65,6 6,8 6,8 6,0 0,,7 65 7,8 0,7 0, -0,5-5,,6 65 0 8, -0,5-0, 0,6-7,, 65,7,,8,7 -,,5 6 8, 8, 6,5 5,, 6, 6 7 5,8,,6,,8 6, 6 6,5 6, 6,6 6,0,5, 6 5 8,0-0, -, -0,8-5,,5 6 5, -5,5-7,0 -, -,5 0,7 6,7,8 0,,0 -,0 5, 6 5, 8,7 5, 7,5,7 6, 6 6 5 month Year sn 0,7, 7, 8,8,6 5,,8,,7 5, 5,6,0,6 0,0 8, 8,,,6,,, 5,6 7,7,6 6 5 5,,0 0,, 8,0 6, 77 8,5 6,, 5,,,5 77 8,5 5,5,5 6, -0,,5 77 8, -,5 -, 0, -,8, 77 8,,7 0,,,,7 76 80,6 8,8 5,6 8,0,6 6, 76 7,0, 5,7 6,, 6,7,7,, 5, 7,,8............... Canonical Correlation Analysis (CCA)
Canonical Correlation Analysis (CCA) The Eigenvectors for all modes and the percentage of the total variance in the observations, using PCA Station no X Y.5 mod. mod.8 mod 0.86 mod 0.5 mod5 78 85-0.88 0.5655 0.080 0.7 0.08 8670 7550-0. -0.080 0.876-0.0 0.88 785 08808-0.6 0.0585 0.50 0.706-0.7 500-0.0-0.0 0.05 0.6-0.77 5 08 76-0.78-0.570 0.6857-0.00 0.0 6 055 680-0. 0.75 0.0860 0.578 0.085 7 700 76-0.8 0.0087 0.06 0.085-0.0867 8 076-0.660-0.677 0.87 0.0-0.565 0 867-0.00-0.08 0.77-0.75 0.508 0 6787 06-0.6-0.878 0.8-0.856 0.88 5 modes 8. % ١٩ Canonical Correlation Analysis (CCA) 00000 000000 0.5 0. 0.0-0. -0.6-0.7 0.00-0. 0.0-0.0-0.0 0. -0.5 0.0-0.0-0.0-0. 800000-0.08 00000 0. 0. 0. -0. 00000-0.07 0.08-0.0-0. 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 The Eigenvectors For mode var explained. of monthly Temperature ٢٠ ١٠
Canonical Correlation Analysis (CCA) 00000 000000 0.08 0.0 0.5 0. 0.7 0. 0.07 0.5 0.0 0. 0. 0.08 0.06 0.6-0.07 0. -0.07 800000-0. 00000-0. -0.0-0.0-0.6 00000-0.7-0. -0.7-0.8 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 The Eigenvectors For mode var explained.8 of monthly Temperature ٢١ Canonical Correlation Analysis (CCA) 00000 000000 0. 0. 0.7 0.6-0.00 0.0 0. -0. -0.0-0. -0. 0.5-0.5-0.0-0.06-0.6-0.0 800000-0.0 00000-0. -0.5-0. -0.08 00000-0.0 0.07-0.0 0.8 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 The Eigenvectors For mode var explained 0.86 of monthly Temperature ٢٢ ١١
00000 0.88 monthly Temperature,.5% 0.7 0.66 0.6 0.6 0.78 0.67 0.80 0.77 0.77 0.88 0.7 0.75 CCA Cor.: 0. 000000 0.6 0.86 0. 0.66 800000 0.75 00000 0. 0.8 0.0 0.7 00000 0.66 0.8 0.75 0.77 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 monthly Precipitation, 67.8% -0.06 70000 60-0.76-0.08 7-0. 80-0.55 6-0. -0.7 H value 80000 60000 0000 0000 00 0000 80000-0.0 00000 0000 0000 60000 80000 ٢٣ 00000 0.88 monthly Temperature,.5% 0.7 0.66 0.6 0.6 0.78 0.67 0.80 0.77 0.77 0.88 0.7 0.75 CCA Cor.: 0. 000000 0.6 0.86 0. 0.66 800000 0.75 00000 0. 0.8 0.0 0.7 00000 0.66 0.8 0.75 0.77 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 monthly Precipitation, 67.8% -0.06 70000 60-0.76-0.08 7-0. 80-0.55 6-0. -0.7 H value 80000 60000 0000 0000 00 0000 80000-0.0 00000 0000 0000 60000 80000 ٢٤ ١٢
00000-0. monthly Temperature,.% -0. -0.8-0. -0.8-0. -0.5-0. -0. -0. -0. -0.5-0. CCA Cor.: 0. 000000-0.5-0.0-0.6-0. 800000-0.5 00000-0.7-0. -0. -0.58 00000-0.70-0.5-0.56-0.58 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 monthly Precipitation,.5% 0. 70000 60 0.5 0.07 7 0.6 80 0.68 6 0.5 0.68 H value 80000 60000 0000 0000 00 0000 80000 0. 00000 0000 0000 60000 80000 ٢٥ 00000-0. monthly Temperature,.% -0. -0.8-0. -0.8-0. -0.5-0. -0. -0. -0. -0.5-0. CCA Cor.: 0. 000000-0.5-0.0-0.6-0. 800000-0.5 00000-0.7-0. -0. -0.58 00000-0.70-0.5-0.56-0.58 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 monthly Precipitation,.5% 0. 70000 60 0.5 0.07 7 0.6 80 0.68 6 0.5 0.68 H value 80000 60000 0000 0000 00 0000 80000 0. 00000 0000 0000 60000 80000 ٢٦ ١٣
Canonical Correlation Analysis (CCA) Zval 5 0 - - - -0,8-0,6-0, -0, 0 0, 0, 0,6 0,8 Zhat Corr : 0.065 ٢٧ Singular Value Decomposition (SVD) ٢٨ ١٤
00000 Homogeneous corr. Map of monthly Temperature -0.7-0.5-0.6-0.7-0.7-0.8-0.7-0. -0. -0. -0.5-0.8-0.6 SVD Cor.: 0. 000000-0.0-0.6-0.7-0.8 800000-0.8 00000-0.5-0. -0.6-0.8 00000-0. -0.7-0.8-0.8 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 Homogeneous corr. Map of monthly Precipitation 0.58 70000 0. 0.85 0.5 0.6 0.88 0.85 80000 60000 0000 0000 0000 80000 0.800000 0000 0000 60000 80000 ٢٩ 00000 Heterogeneous corr. Map of monthly Temperature -0. -0. -0.5-0. -0.8-0. -0.8-0. -0. -0. -0. -0. -0.8 SVD Cor.: 0. 000000-0. -0.0-0. -0. 800000-0.0 00000-0.7-0. -0. -0. 00000-0. -0. -0. -0.6 800000 000000 00000 00000 00000 800000 000000 00000 00000 600000 800000 Heterogeneous corr. Map of monthly Precipitation 0. 70000 0.0 0. 0.8 0.7 0.7 0. 80000 60000 0000 0000 0000 80000 0.8 00000 0000 0000 60000 80000 ٣٠ ١٥
Thank you ٣١ ١٦