Applied Multivariate analysis on Precipitation and Temperature data (Barada and Awaj Basin)

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Gordon Norman
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org - ACSAD,Syria April 00, Damascus From -8 April.

1 Training Workshop on Environment Statistics Damascus, -8 April 00 Applied on Precipitation and Temperature data (Barada and Awaj Basin) Mazen Abou Abdallah - 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

2 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, , 7,8,5, 6, 7 6 5, 5, 8,6 85,6, 07,5 6 76,7 67,,5,6 08,5 6, 77, ,7,,5 8,, 7, 6 6,5, 0, 0,5, 5,, 7 6, 6, 50, 0 8, 7,8 0,, ,, 8,6,5, 5,, 5, 65 76,7 0,, 0,5, 6,5 6, ,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, , 7,6, 07 5, 0,7 5, ,5, 8,, 6,, 7, ,8, 7, 5 7, 8,7,6 00, , 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

3 Principal Component Analysis (PCA) Precipitation Chart of Station no.: Annual Precipitation ( mlm ) Date ٥ Principal Component Analysis (PCA) Precipitation Chart of Station no.: Annual Precipitation ( mlm ) Date ٦ ٣

4 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. mod modes 6.8 % ٧ Principal Component Analysis (PCA) The Distribution of topographic levels ٨ ٤

5 Principal Component Analysis (PCA) Eigenvector The Eigenvectors For mode var explained 67.8 ٩ Principal Component Analysis (PCA) Eigenvector The Eigenvectors For mode var explained.5 ١٠ ٥

6 Principal Component Analysis (PCA) Eigenvector 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 modes 0 % ١٢ ٦

7 ٧ ١٣ Rotated Principal Component Analysis (PCA) Rotated PCA - The Eigenvectors For mode var explained Eigenvector ١٤ Extended Principal Component Analysis (PCA) mod7 mod6 mod5 mod mod mod mod Level Y X Station no

8 Extended Principal Component Analysis (PCA) Nov Dec Jan Mode Var Exp 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. mod modes 6.8 % ١٦ ٨

9 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,, , -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, 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, ,,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,,,, ,6 8,8 5,6 8,0,6 6, 76 7,0, 5,7 6,, 6,7,7,, 5, 7,, Canonical Correlation Analysis (CCA)

10 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 mod modes 8. % ١٩ Canonical Correlation Analysis (CCA) The Eigenvectors For mode var explained. of monthly Temperature ٢٠ ١٠

11 Canonical Correlation Analysis (CCA) The Eigenvectors For mode var explained.8 of monthly Temperature ٢١ Canonical Correlation Analysis (CCA) The Eigenvectors For mode var explained 0.86 of monthly Temperature ٢٢ ١١

12 monthly Temperature,.5% CCA Cor.: monthly Precipitation, 67.8% H value ٢٣ monthly Temperature,.5% CCA Cor.: monthly Precipitation, 67.8% H value ٢٤ ١٢

13 monthly Temperature,.% CCA Cor.: monthly Precipitation,.5% H value ٢٥ monthly Temperature,.% CCA Cor.: monthly Precipitation,.5% H value ٢٦ ١٣

14 Canonical Correlation Analysis (CCA) Zval ,8-0,6-0, -0, 0 0, 0, 0,6 0,8 Zhat Corr : ٢٧ Singular Value Decomposition (SVD) ٢٨ ١٤

15 00000 Homogeneous corr. Map of monthly Temperature SVD Cor.: Homogeneous corr. Map of monthly Precipitation ٢٩ Heterogeneous corr. Map of monthly Temperature SVD Cor.: Heterogeneous corr. Map of monthly Precipitation ٣٠ ١٥

16 Thank you ٣١ ١٦

The South American monsoon system and the 1970s climate transition L. M. V. Carvalho 1, C. Jones 1, B. Liebmann 2, A. Silva 3, P. L.

The South American monsoon system and the 1970s climate transition L. M. V. Carvalho 1, C. Jones 1, B. Liebmann 2, A. Silva 3, P. L. Silva Dias 3 1 University of California Santa Barbara 2 CIRES, NOAA,