Determining Climate Extremes of Mosul Weather Using Robust Noise Clustering Strategy.
Abstract
The paper aims to analyse the climatic data of the city of Mosul during the summer season from 2013 to 2022, focusing on the maximum temperature variable. Modern methods have been used to detect climate fluctuations that have not been used previously, adapt them to the study data, and explore the general and extreme climates that any of the previous studies have not touched. Variable clustering techniques have been used to discover the latent components according to the local groups model. The "K+1" noise group strategy was used to identify high-noise variables. The researcher proposed a wide format for ordering the data: P > N, which means that the number of variables is greater than the number of observations. The observations represented the school years; the variables were the summer days for three months (June, July, and August). This arrangement proved suitable for the variable aggregation technique of high-dimensional data. The results showed six groups, five of which were almost homogeneous. The five clusters indicate different patterns of maximum temperature increases during the summer. The first cluster highlights heat waves in mid-summer (July and August), while the second cluster focuses on the hot ends of summer (late June and August). The third cluster refers to early and continuous heat waves in June and July, while the fourth cluster reflects persistent heat in late July and August. The fifth cluster shows a variation in temperatures between the beginning and the end of summer. The excluded noise variables represent inconsistent data or outliers that did not belong to any cluster. This contributes to improving the accuracy of climate models. The results highlight characteristic climatic patterns and provide recommendations for strengthening environmental and agricultural planning.
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