Open AccessA NON-GAUSSIAN MODEL FOR INDIAN MONSOON RAINFALL
Author(s) -
K. J. Ramesh,
Radhika Iyengar
Publication year - 2017
Publication title -
international journal of research - granthaalayah
Language(s) - English
Resource type - Journals
eISSN - 2394-3629
pISSN - 2350-0530
DOI - 10.29121/granthaalayah.v5.i4rast.2017.3305
Subject(s) - hermite polynomials , gaussian , probability density function , gaussian function , mathematics , histogram , gaussian process , gaussian random field , generalized inverse gaussian distribution , gaussian filter , statistical physics , function (biology) , series (stratigraphy) , statistics , mathematical analysis , physics , computer science , image (mathematics) , geology , quantum mechanics , paleontology , artificial intelligence , evolutionary biology , biology
A non-Gaussian model as a function of Gaussian process is developed in this paper for Indian monsoon rainfall time series. The functions of a Gaussian process are the Hermite polynomials. The unknown coefficients of the Hermite polynomials are found with the help of the first four moments of the given data. Since the probability density function of the Gaussian process is known, the non-Gaussian density function for the rainfall process is found by using the transformation on the known Gaussian density function numerically. Sample histogram of the data and the non-Gaussian density function are compared graphically along with the Gaussian density function. This clearly justifies that the non-Gaussian density better compares with the data distribution. This exercise has been done on the four broad regions of India identified by Indian Meteorological Department (IMD) and also for one subdivision of Karnataka. It has been observed that at 5% significance level, this model is able to reproduce the probability structure of the rainfall time series at different spatial scales studied.