A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator

The need for the development of a method for generating an ensemble of rainfall scenarios, which are conditioned on the observed rainfall, and its place in the HYREX programme is discussed. A review of stochastic models for rainfall, and rainfall forecasting techniques, is followed by a justificatio...

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Main Authors: D. Mellor, J. Sheffield, P. E. O'Connell, A. V. Metcalfe
Format: Article
Language:English
Published: Copernicus Publications 2000-01-01
Series:Hydrology and Earth System Sciences
Online Access:http://www.hydrol-earth-syst-sci.net/4/603/2000/hess-4-603-2000.pdf
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spelling doaj-991feadda5e6489692aade82fdd905782020-11-24T21:39:34ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382000-01-0144603615A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generatorD. MellorJ. SheffieldP. E. O'ConnellP. E. O'ConnellA. V. MetcalfeThe need for the development of a method for generating an ensemble of rainfall scenarios, which are conditioned on the observed rainfall, and its place in the HYREX programme is discussed. A review of stochastic models for rainfall, and rainfall forecasting techniques, is followed by a justification for the choice of the Modified Turning Bands (MTB) model in this context. This is a stochastic model of rainfall which is continuous over space and time, and which reproduces features of real rainfall fields at four distinct scales: raincells, cluster potential regions, rainbands and the overall outline of a storm at the synoptic scale. The model can be used to produce synthetic data sets, in the same format as data from a radar. An inversion procedure for inferring a construction of the MTB model which generates a given sequence of radar images is described. This procedure is used to generate an ensemble of future rainfall scenarios which are consistent with a currently observed storm. The combination of deterministic modelling at the large scales and stochastic modelling at smaller scales, within the MTB model, makes the system particularly suitable for short-term forecasts. As the lead time increases, so too does the variability across the set of generated scenarios.</p> <p style='line-height: 20px;'><b>Keywords: </b>MTB model, space-time rainfall field model, rainfall radar, HYREX, real-time flow forecasting</p>http://www.hydrol-earth-syst-sci.net/4/603/2000/hess-4-603-2000.pdf
collection DOAJ
language English
format Article
sources DOAJ
author D. Mellor
J. Sheffield
P. E. O'Connell
P. E. O'Connell
A. V. Metcalfe
spellingShingle D. Mellor
J. Sheffield
P. E. O'Connell
P. E. O'Connell
A. V. Metcalfe
A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
Hydrology and Earth System Sciences
author_facet D. Mellor
J. Sheffield
P. E. O'Connell
P. E. O'Connell
A. V. Metcalfe
author_sort D. Mellor
title A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
title_short A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
title_full A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
title_fullStr A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
title_full_unstemmed A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
title_sort stochastic space-time rainfall forecasting system for real time flow forecasting i: development of mtb conditional rainfall scenario generator
publisher Copernicus Publications
series Hydrology and Earth System Sciences
issn 1027-5606
1607-7938
publishDate 2000-01-01
description The need for the development of a method for generating an ensemble of rainfall scenarios, which are conditioned on the observed rainfall, and its place in the HYREX programme is discussed. A review of stochastic models for rainfall, and rainfall forecasting techniques, is followed by a justification for the choice of the Modified Turning Bands (MTB) model in this context. This is a stochastic model of rainfall which is continuous over space and time, and which reproduces features of real rainfall fields at four distinct scales: raincells, cluster potential regions, rainbands and the overall outline of a storm at the synoptic scale. The model can be used to produce synthetic data sets, in the same format as data from a radar. An inversion procedure for inferring a construction of the MTB model which generates a given sequence of radar images is described. This procedure is used to generate an ensemble of future rainfall scenarios which are consistent with a currently observed storm. The combination of deterministic modelling at the large scales and stochastic modelling at smaller scales, within the MTB model, makes the system particularly suitable for short-term forecasts. As the lead time increases, so too does the variability across the set of generated scenarios.</p> <p style='line-height: 20px;'><b>Keywords: </b>MTB model, space-time rainfall field model, rainfall radar, HYREX, real-time flow forecasting</p>
url http://www.hydrol-earth-syst-sci.net/4/603/2000/hess-4-603-2000.pdf
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