Articles | Volume 4, issue 4
Hydrol. Earth Syst. Sci., 4, 603–615, 2000
https://doi.org/10.5194/hess-4-603-2000

Special issue: HYREX: the HYdrological Radar EXperiment

Hydrol. Earth Syst. Sci., 4, 603–615, 2000
https://doi.org/10.5194/hess-4-603-2000

  31 Dec 2000

31 Dec 2000

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

D. Mellor1, J. Sheffield1, P. E. O'Connell1, and A. V. Metcalfe2 D. Mellor et al.
  • 1Water Resource Systems Research Laboratory, Department of Civil Engineering, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK
  • 2Department of Engineering Mathematics, University of Newcastle upon Tyne, Newcastle upon Tyne, NE1 7RU, UK
  • e-mail for corresponding author: P.E.O'Connell@newcastle.ac.uk

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.

Keywords: MTB model, space-time rainfall field model, rainfall radar, HYREX, real-time flow forecasting