Improved fitting of the Bartlett-Lewis Rectangular Pulse Model for hourly rainfall
Open Access
- 1 December 1994
- journal article
- research article
- Published by Taylor & Francis in Hydrological Sciences Journal
- Vol. 39 (6) , 663-680
- https://doi.org/10.1080/02626669409492786
Abstract
This paper examines the applicability of the Bartlett-Lewis Rectangular Pulse Model to rainfall taken from a site in Elmdon, Birmingham, UK. The approach used is to assess the performance of the model in terms of characteristics of the precipitation process, incorporating monthly seasonality. Analytical expressions are derived to complement those presented in Rodriguez-Iturbe et al. (1987). As in that paper, the shortcomings of the simple Poisson process are reduced by the use of a Bartlett-Lewis process. Different methods of parameter estimation are examined. The characteristic features of the time distribution of rainfall events, however, can be well approximated only by optimization and this enables an improved identification of the model parameters.Keywords
This publication has 12 references indexed in Scilit:
- Modelling of British rainfall using a random parameter Bartlett-Lewis Rectangular Pulse ModelJournal of Hydrology, 1993
- Further developments of the neyman‐scott clustered point process for modeling rainfallWater Resources Research, 1991
- A simple stochastic model of hourly rainfall for Farnborough, EnglandHydrological Sciences Journal, 1990
- Probabilistic representation of the temporal rainfall process by a modified Neyman‐Scott Rectangular Pulses Model: Parameter estimation and validationWater Resources Research, 1989
- Rectangular pulses point process models for rainfall: Analysis of empirical dataJournal of Geophysical Research: Atmospheres, 1987
- Scale of fluctuation of rainfall modelsWater Resources Research, 1986
- Continuous‐Time Versus Discrete‐Time Point Process Models for Rainfall Occurrence SeriesWater Resources Research, 1986
- Scale considerations in the modeling of temporal rainfallWater Resources Research, 1984
- A point process model of summer season rainfall occurrencesWater Resources Research, 1983
- A stochastic cluster model of daily rainfall sequencesWater Resources Research, 1981