A stochastic space-time rainfall forecasting system for real time flow forecasting I: Development of MTB conditional rainfall scenario generator
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 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.
HYREX, MTB model, Rainfall radar, Real-time flow forecasting, Space-time rainfall model
603-615
Mellor, D.
473c8d31-71b8-4a7e-8202-ae33da69ca00
Sheffield, J.
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
O'Connell, P. E.
e2364595-d542-4de5-af46-866b09c16033
Metcalfe, A. V.
f3e7156d-b9a5-417d-b486-f5dfa3ee10ef
December 2000
Mellor, D.
473c8d31-71b8-4a7e-8202-ae33da69ca00
Sheffield, J.
dd66575b-a4dc-4190-ad95-df2d6aaaaa6b
O'Connell, P. E.
e2364595-d542-4de5-af46-866b09c16033
Metcalfe, A. V.
f3e7156d-b9a5-417d-b486-f5dfa3ee10ef
Mellor, D., Sheffield, J., O'Connell, P. E. and Metcalfe, A. V.
(2000)
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, 4 (4), .
(doi:10.5194/hess-4-603-2000).
Abstract
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.
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Published date: December 2000
Keywords:
HYREX, MTB model, Rainfall radar, Real-time flow forecasting, Space-time rainfall model
Identifiers
Local EPrints ID: 480409
URI: http://eprints.soton.ac.uk/id/eprint/480409
ISSN: 1027-5606
PURE UUID: 6fbe9aa8-d4e0-4137-846d-ef11024db501
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Date deposited: 01 Aug 2023 21:43
Last modified: 11 May 2024 01:48
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Contributors
Author:
D. Mellor
Author:
P. E. O'Connell
Author:
A. V. Metcalfe
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