The Probability Distribution of Maximum Temperature to Assess the Suitable Statistical Models: Take the North-East and Southern Regions of Pakistan

Abstract

Precise maximum temperature probability distribution information is indeed of accurately significance for numerous temperature uses. The purpose of this research to assess the appropriateness of these functions likelihood for evaluating the temperature models at different sites in southern part of Pakistan. The Kumaraswamy distribution function is used initially to approximation the models of maximum temperature. Compare the presentation of the Kumaraswamy distribution with twelve commonly used the probability functions. The consequences obtained show that the more effective functions are not similar across all sites. The maximum temperature features, quality and quantity of the noted temperature observation can be regarded as a factors that affect the presentation of the function. Similarly, the skewness of the noted maximum temperature observations may affect the precision of Kumaraswamy distribution. For the Hyderabad, Lahore and Sialkot sites, the Kumaraswamy distribution obtainable the topmost presentation, however for the Karachi, Multan stations, the generalized extreme value (GEV) distributions provided the best fit, respectively. According to the calculations, the Kumaraswamy distribution usually be regarded as a valid distribution because it runs 3 best fit sites and ranks 2 to 3 among the remaining sites. Though, the tight presentation of the Kumaraswamy and GEV and the flexibility of the Weibull distribution which has been usually verified, more evaluations of the presentation of the Kumaraswamy distribution are needed.

Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia

“…Probability Distribution Functions (PDFs), standard deviations, means and Cumulative Distribution Functions (CDFs) were analysed to ascertain the effects of climate change, biophysical and economics on maize yield, sowing date, cultivar and N fertilizer rate. Understanding the PDFs for rainfall and temperature is important for their characterization [67]. A PDF, is a parametric method for estimating the probability of occurrence of a random value within a particular range in a dataset.…”