SAEED RASEKHI, ISIDRO A. PÉREZ, M. ÁNGELES GARCÍA, FATEMEH PAZOKI
KEYWORDS : Temperature trend; harmonic analysis; distribution functions, probability distribution functions; urban climate.
Abstract :
Temperature trends and fluctuations provide key insights into climate change dynamics. This study analyzes historical temperature data for Berlin, Germany, between 1990 and 2022, using the European Meteorological Observations (EMO) Project dataset, which provides gridded temperature data at a 1 arcmin resolution. The daily maximum and minimum temperatures were calculated using spatial averaging over the urban area of Berlin, considering all grid points within the geographical boundary (12.87°E, 52.20°N to 13.96°E, 52.79°N). The study employs Fourier Transform analysis, linear regression modeling, and least squares fitting to examine temperature fluctuations and identify dominant periodic cycles. Statistical analysis classifies April to September as the warm season, with a median temperature difference of 10.3°C (min temp) and 14.2°C (max temp) between warm and cold months. Probability distribution fitting reveals that beta and normal distributions best represent the observed temperature variations. A comparison with the TRY project dataset (1995–2012) indicates an increasing trend: 0.55°C per decade for maximum temperature and 0.30°C per decade for minimum temperature. This work highlights the growing temperature trends in Berlin and supports further studies on urban heat dynamics and climate adaptation.