ebrahim ahmadzadeh

In this paper, the Poisson Point Process model has been used to analyze the frequency and intensity of hot and cold extreme temperatures at Abali station. The Poisson Point Process Model (Paritou- Poisson) is designed based on Peaks over threshold method. In this method, a suitable temperature threshold was first selected using statistical methods and then the Paritou-Poisson distribution was fitted to values higher than the threshold.  Estimation of distribution parameters was carried out using Maximum likelihood Method and extreme temperatures were estimated for different return periods. To validate the model, Q-Q charts and χ2 statistics were used. The results showed that the Poisson point process model has a great potential for modeling the extreme temperatures

Recently, high extreme and frequency distribution of higher sequence of precipitation have been attended more. Through this, because of geographical characteristics of each area, diverse and different thresholds have been presented and utilized for the mentioned precipitation’s characteristics. Through the present research, for exploring and analyzing the extreme precipitation event in Tehran through the 1983-2016 statistical periods, some of the indexes presented by World Meteorological Organization Committee were utilized.
Data and

The extreme precipitation event distribution sequence behavior and frequency analyses and severity of the mentioned event were explored by using the Peak Over Threshold value model (POT). Selecting the suitable threshold for calculating the peaks higher than threshold using the 95 percentile methods, Mean of Remaining Life (MRL) and Model-Base Controlling (MBC) was done. Distribution parameters’ determining by maximum likelihood method and evaluating the occurrence and extreme events’ severity was investigated by using the Average Returning Intervals (ARLs).

The results accessed from Man-Kendal illustrated that in the studying stations, the significance process in the extreme indexes through the 1983-2016 was not met.

Exploring the development curves showed that through the 34-year-old statistical period (1983-2016), most of the observed extreme events in the stations had 1 to 10 years return. Q-Q exploring charts and χ2 statistics showed that POT model included the high quality for modeling the extreme events in the studying area