The effect of the relative humidity and the specific humidity on the determination of the climate regions in Turkey

All elements of climate that affect climatic events must be taken into account such that the climate regions are determined with exactitude. To this end, data on maximum temperature (Tx), minimum temperature (Tn), mean temperature (Tm), and precipitation (Pt) as well as local pressure (Ps), mean wind (WN), relative humidity (RH), and specific humidity (SH) have been investigated statistically and graphically. The specific humidity data calculated using Tm, Ps, and RH data and statistical comparisons have shown that there are no drawbacks to using SH in climatologic studies. According to principal component analysis, it was concluded that RH and SH should be used together with Tx, Tm, Tn, and Pt for the determination of the climate regions. Two cluster analysis methods, Ward's method and Kohonen neural network technique, were used to show the effect of RH and SH. A comparison of the cluster's stability between the limited and high number of stations shows that Ward's method and Kohonen neural network are very stable in both cases. It was also determined that RH does not change the outline of climate regions but that it affects the zones of climate transition. It was observed that clusters determined by using Tm, Pt, and RH provide relatively more distinctive clusters in the data space than clusters determined by using Tm, Pt, and SH.

[1]  B. E. Davis.  Gis a Visual Approach , 1996 .

[2]  M. Türkeş,et al.  SPATIAL AND TEMPORAL ANALYSIS OF ANNUAL RAINFALL VARIATIONS IN TURKEY , 1996 .

[3]  T. Kohonen.  Self-Organized Formation of Correct Feature Maps , 1982 .

[4]  Victor J. Yohai,et al.  A Bivariate Test for the Detection of a Systematic Change in Mean , 1978 .

[5]  T. Schneider.  Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values. , 2001 .

[6]  G. Trewartha.  An Introduction To Weather And Climate , 1938 .

[7]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[8]  Sirri Erinç.  The Climates of Turkey According to Thornthwaite's Classifications , 1949 .

[9]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[10]  G. Können,et al.  Homogeneity of 20th century European daily temperature and precipitation series , 2003 .

[11]  Yurdanur S. Unal,et al.  Redefining the climate zones of Turkey using cluster analysis , 2003 .

[12]  M. Türkeş,et al.  Precipitation changes and variability in Turkey linked to the North Atlantic oscillation during the period 1930–2000 , 2003 .

[13]  Leo R Beard,et al.  Statistical Methods in Hydrology , 1962 .

[14]  M. Türkeş,et al.  Use of the spectral clustering to determine coherent precipitation regions in Turkey for the period 1929–2007 , 2011 .

[15]  C. W. Thornthwaite.  An approach toward a rational classification of climate. , 1948 .

[16]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[17]  A. E. Gill.  Atmosphere-Ocean Dynamics , 1982 .

[18]  M. Türkeş.  Vulnerability of Turkey to Desertification With Respect to Precipitation and Aridity Conditions , 1999 .

[19]  T. Kohonen.  Self-organized formation of topology correct feature maps , 1982 .

[20]  R. Fovell,et al.  Climate zones of the conterminous United States defined using cluster analysis , 1993 .

[21]  M. Türkeş.  Influence of geopotential heights, cyclone frequency and southern oscillation on rainfall variations in Turkey , 1998 .

[22]  M. Türkeş,et al.  Climatological responses of winter precipitation in Turkey to variability of the North Atlantic Oscillation during the period 1930–2001 , 2005 .

[23]  M. Türkeş,et al.  Spatiotemporal variability of precipitation total series over Turkey , 2009 .

[24]  L. Kalkstein,et al.  An Evaluation of Three Clustering Procedures for Use in Synoptic Climatological Classification , 1987 .