Annual Mean Temperature and Rain Precipitation in North America Using NHPP to Detect Climate Changes

In this study, non-homogeneous Poisson processes (NHPP) are assumed to analyze annual average temperatures and rain precipitations, considering climate data for some regions of North America reported a long period. A power law process (PLP) is the intensity function (derivative mean value function) or rate \(\lambda\) (t), t \(\ge\) 0 NHPP which events occur (accumulated number years in given time interval [0,t) where measure above threshould by overal period) presence not change-point. The parameters model estimated under Bayesian approach using MCMC (Markov Chain Monte Carlo) methods. Alternatively use PLP process, we also assume polynomial parametrical form simple inference proposed get better fit functions process. From fitted models it was possible detect changes occurred.