Abstract
It is a scientifically novel insight to classify the climate of a region using empirical methods together with clustering technique for practical usage in agricultural and industrial sectors. The main objective of this study is to compare the empirical approach to climate classification (Thornthwaite and Mather, De Martonne, the Extended De Martonne and the IRIMO (I.R. of Iran Meteorological Organization)) with clustering technique, Ward’s hierarchical agglomerative method over Iran. The maximum and minimum temperatures and precipitation data of 356 weather stations are used from IRIMO databases. 35 synoptic weather stations are selected for detailed inspection based on appropriate geographical distribution and availability of a continuous 50-year data (1966–2015). Compared with the three empirical reference methods of climate classification, the Thornthwaite and Mather method clearly shows the role of water bodies and air masses for determining the climate type in different regions. This factor is identified as the main advantage of this method over the three others. This superiority is the most visible for the highlands/mountainous regions, in the vicinity of the Zagros Mountains, and in the western regions of Iran. As a case in point, while in the De Martonne and the Extended De Martonne methods, the Zagros storm cell is climatically classified similar to patchy areas in Caspian Sea coastal zone, this cell is correctly identified as a separate zone in the Thornthwaite and Mather method. The results revealed that the clusters obtained from Ward’s algorithm are comparable to those of empirical climate classifications, particularly Thornthwaite and Mather method.
Climate classification in the Northern Hemisphere using envelopes of temperature signals