An approach to spatio-temporal analysis for climatic data

Abstract

This work considers a large-scale multisite investigation of the effects of sunshine duration on Algerian territory. It also studies the correlation between sunshine duration and commonly used climatic parameters (temperature, water vapour pressure, evaporation, relative humidity and rainfall). Two data sets have served this purpose. The first one consists of sunshine duration measurements of the whole Algerian weather network over the period 1960-2002 while the second set provides climatic parameters collected in Dar El-Beida, Algiers (36° 41'N, 03°13' E) over the period 1950-2008. This station is the best in terms of fulfilling criteria such as long time data series (at least 50 years) as well as reliable measurements. To achieve the expected goal, we use an appropriate objective clustering method, named Principal Component Analysis (PCA) with a coupling of a Hierarchical Ascending Clustering (HAC) algorithm. PCA is often used not only for reducing the data before the actual clustering is carried out but also because it might help identifying the characteristics of the clusters. In this way, we establish a distribution of weather stations and identify the main homogeneous areas of the country that are more distinguishable and useful to our purpose as well as we classify the months of year. Obtained results divide Algeria into three distinct climate regions which daily monthly means of sunshine duration are also studied. Further information can be drawn through maps established by ‘MapInfo’ Software. According to the correlation matrix, it also turns out that sunshine duration strongly influences climatic parameters.