Studies on extreme temperature are beneficial to human understanding of extreme events. Decision-makers and researchers in climatology will benefit from knowledge about the behaviour of extreme temperature, as appropriate policies and plans can be drawn to prepare the general public for changes due to extreme temperature. This can be achieved through Extreme Value of Analysis (EVA) by fitting of probability distributions to the recorded surface temperature data. This paper illustrates the adoption of Extreme Value Type-1 (EV1), Extreme Value Type-2 (EV2), Log Normal (LN2) and Log Pearson Type-3 (LP3) distributions in EVA of temperature for Hissar. Method of moments and Maximum Likelihood Method (MLM) are used for determination of parameters of EV1, EV2, LN2 and LP3 distributions. In addition to above, method of least squares and order statistics approach are used for determination of parameters of EV1 and EV2 distributions. Goodness-of-Fit tests such as Anderson-Darling and Kolmogorov-Smirnov and diagnostic test using D-index are applied for evaluation of parameter estimation methods of probability distributions adopted in EVA. The paper presents the LN2 (MLM) is better suited probability distribution for modelling the annual maximum temperature data recorded at Hissar whereas LP3 (MLM) for annual minimum temperature.
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