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You are here : eLibrary : IAHR World Congress Proceedings : 32nd Congress - Venice (2007) : THEME B: Data Acquisition and Processing For Scientific Knowledge and Public Awareness. : Rainfall downscaling and stochastic generation
Rainfall downscaling and stochastic generation
Author : Stefano Zanetti, Marco Marani
This paper summarizes some recent theoretical and experimental results concerning the temporal correlation structure of rainfall and illustrates their possible applications to rainfall downscaling. A realistic description of land-atmosphere interactions in climate and hydrologic studies requires the specification of the rainfall forcing at aggregation scales of one hour or less. This is in contrast with the wide availability of daily rainfall observations and with the typically coarse output resolution of climate and numerical weather forecast models. Downscaling techniques are thus required to estimate high resolution temporal series or precipitation fields on the basis of coarse resolution output obtained by numerical models. Here we present a method for downscaling rainfall in time using theoretically-based estimates of rainfall variability at the hourly scales from daily statistics. The method is validated on a wide data set representative of different rainfall regimes and produces unbiased estimates of rainfall variance at the hourly scale when a power-law-tailed autocorrelation is used for the rainfall process. We then calibrate a Bartlett-Lewis rainfall stochastic model using the estimated hourly variance and use it to generate synthetic hourly rainfall sequences which are shown to reproduce the observed small-scale variability. Generated and observed hourly extreme events are finally compared.
File Size : 495,229 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 32nd Congress - Venice (2007)
Article : THEME B: Data Acquisition and Processing For Scientific Knowledge and Public Awareness.
Date Published : 01/07/2007
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