Skip navigation

Parsimonious modelling of winter season rainfall incorporating reanalysis climatological data

Parsimonious modelling of winter season rainfall incorporating reanalysis climatological data

Garthwaite, Andrew P. and Ramesh, N. I. (2018) Parsimonious modelling of winter season rainfall incorporating reanalysis climatological data. Hydrology Research, 49 (6). pp. 2030-2045. ISSN 0029-1277 (doi:https://doi.org/10.2166/nh.2018.012)

[img]
Preview
PDF (Corrected Proof Copy - Open Access)
20883 RAMESH_Parsimonious_Modelling_of_Winter_Season_Rainfall_2018.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (1MB) | Preview

Abstract

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.

Item Type: Article
Additional Information: © 2018 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Uncontrolled Keywords: bucket-tip, covariates, Cox model, fine-scale modelling, seasonal rainfall
Subjects: Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 15 Apr 2019 15:52
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 4
URI: http://gala.gre.ac.uk/id/eprint/20883

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics