RT info:eu-repo/semantics/article
T1 Trend and the cycle of fluctuations and statistical distribution of temperature of Berlin, Germany, in the period 1995–2012
A1 Rasekhi, Saeed
A1 Pérez Bartolomé, Isidro Alberto
A1 García Pérez, María Ángeles
A1 Pazoki, Fatemeh
K1 Temperature
K1 Temperatura atmosférica
K1 Climatology
K1 Meteorology
K1 Harmonic analysis
K1 Análisis armónico
K1 Statistics
K1 Climatología - Modelos estadísticos
K1 Probabilities
K1 Distribución (Teoría de probabilidades)
K1 Germany
K1 Alemania
K1 Atmospheric sciences
K1 2502 Climatología
K1 2509 Meteorología
K1 1209 Estadística
K1 2501 Ciencias de la Atmósfera
AB 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.
PB MDPI
SN 2673-4931
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/67908
UL https://uvadoc.uva.es/handle/10324/67908
LA eng
NO Environmental Sciences Proceedings, 2023, Vol. 27, Nº. 1, 5
NO Producción Científica
DS UVaDOC
RD 26-jun-2024