Parsimonious modelling of winter season rainfall incorporating reanalysis climatological data | Zendy

Andrew Garthwaite | Zendy; N. I. Ramesh | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Parsimonious modelling of winter season rainfall incorporating reanalysis climatological data

Author(s) -

Andrew Garthwaite,

N. I. Ramesh

Publication year - 2018

Publication title -

hydrology research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.665

H-Index - 48

eISSN - 1996-9694

pISSN - 0029-1277

DOI - 10.2166/nh.2018.012

Subject(s) - environmental science , covariate , climatology , scale (ratio) , relative humidity , poisson distribution , markov chain , meteorology , atmospheric sciences , statistics , mathematics , geography , geology , cartography

Several Markov Modulated Poisson Process (MMPP) models are developed to describe winter season rainfall with parsimonious parameter use. We propose a methodology for determining the best form of seasonal model for fine-scale rainfall within a MMPP framework. Of those proposed here, a model with a fixed transition rate is shown to be superior over the other MMPP models considered. The model is expanded to include covariate data for sea-level air pressure, relative humidity, and temperature using reanalysis data over 14 years from the coordinates covering the Bracknell rainfall collection site in England. Results are compared using the likelihood ratio test and the second-order properties of aggregated rainfall.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research