A probability distribution for precipitation data analysis

Hydrologic design is often based on assessments of large return interval measures; it is vital to be able to conclude them as precisely as possible. Henceforth, the selection of a probability distribution is very crucial for such cases. In view of this scenario, we propose and study a pliant probability distribution for precipitation data analysis. Some mathematical and statistical properties are analyzed. In order to make stronger predictions and judge the realistic return period, we have also characterized the model via Laplace transformation. We have estimated its parameters via the maximum likelihood estimation and constructed its information matrix for developing the confidence belt of population parameters. Moreover, a real-life setup is also considered by applying the model over precipitation data of diverse regions, including Jacksonville, Florida (USA), Barkhan (Pakistan), British Columbia (Canada), and Alexandria (Egypt). This investigated study is based on various statistical parametric and nonparametric tests, which indicates that the proposed model is one of the better strategies for precipitation data analysis when compared with the famous three-parameter Kappa model.

Automated summary

This paper proposes a pliant probability distribution for precipitation data analysis and analyzes its mathematical and statistical properties. The model is characterized via Laplace transformation and its parameters are estimated through maximum likelihood estimation. The model is applied to precipitation data in different regions and compared with the famous three-parameter Kappa model, showing better performance.