Temperature, as one of the most important factors in meteorological data analysis, is a variable parameter with severe changes in different periods. The trend of temperature changes over time is also particularly important to investigating climate change. In this research, using the data from the TRY Project, which includes meteorological data with an accuracy of 1 km grid and a time accuracy of 1 hour, the temperature parameter of the city of Berlin is selected and the average temperature of the urban area of Berlin was calculated at different temporal scales. In addition to finding the linear regression trend of average annual temperature increase, Fourier transforms analysis and the least squared error fitting method was used to investigate harmonic temperature fluctuations to find the main sinusoidal period. Further, with the statistical analysis of data in daily averages and 1 h intervals by considering medians of data as the benchmark for classification, months from April to October were determined as the hot months of the year, and hours from 9 to 19 were determined as daytime. Based on the mentioned classification, it was found that while the median difference between hot and cold months is more than 12 °C, the median difference between days and nights for the hot and cold months’ data is 5.2 °C and 2.1 °C, respectively. With this classification, the probability distribution of temperature was studied for each group, and the degree of similarity of this distribution with probability distribution functions such as normal, beta, gamma, and cosine, were investigated. The separate analysis of the data categorized by this method had the highest degree of similarity with beta and normal functions.
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