| Abstract [eng] |
Extreme temperatures are related to unusually hot or cold weather, which has a significant effect on the human, nature and important objects. They are fairly common phenomenon not only in the world but also in Lithuania. Thus there are important to perform probabilistic and statistical analyzes to prepare for and adapt to the possibly even larger temperature values and changes. It is important to carry out relevant research in areas with critical infrastructures and dangerous objects (e.g.: nuclear power plant, the company that makes nitrogen fertilizer “Achema”, phosphate fertilizer manufacturing company “Lifosa”). Applying probabilistic models and statistical methods the occurrence of extreme temperatures are evaluated calculating return periods and probabilities of temperature exceedance. Main objective of this work is probabilistic assessment of extreme temperature dynamics, which is carried out for whole Lithuania and specifically for Dūkštas region. The other main purpose of this work is to analyse the changes of extreme temperatures. The probabilistic analysis of extreme temperatures in Lithuanian territory is based on historical data taken from Lithuanian Hydrometeorology Service, Dūkštas Meteorological Station, Lithuanian Energy Institute and INNP Environmental Protection Department of Environmental Monitoring Service. Probabilistic assessment is focused on an application and comparison of Gumbel, Weibull and Generalized Value (GEV) distribution, enabling to select a distribution, which has the best fit for data of extreme temperatures. The probabilistic assessment is based on mathematical model, which was applied taking into account statistical data from 1961 up to 2014. In order to estimate likelihood of the annual minimum and maximum temperatures (based on 1961-2014 data) the probabilistic assessment using the extreme value distributions was performed, in particular, for each sample the best extreme value distribution was identified. In addition, to the previously mentioned study of dry bulb temperature extremes, wet bulb temperature rating, which enables to determine the relative humidity, was also carried out. Using the selected Gumbel distribution, the local temperature data analysis in eastern Lithuania, i.e. in Dūkštas, region was conducted. Then temperature variation analysis using the moving average method was carried out and the extremes changes in view of the uncertain data were investigated. The probabilistic assessment showed that using all extreme value distributions the return period for the annual minimum temperature of -30 °C (extremes events criterion) is 3 years. The Lithuanian record (-38 °C, during 1970 recorded in Utena) return period considering 18 meteorological stations is 26 (Gumbel), 62 (Weibull) and 63 (Generalized) years. The probabilistic assessment showed that using all extreme value distributions the return period for the annual maximum temperature of 30 °C (extremes events criterion) is only about 1 year. For the similar purposes this work can be used not only for extreme temperatures, but also for the assessment of other extreme phenomena or events. |
