Editorial Type:
Article
Article Type:
Research Article

RainFARM: Rainfall Downscaling by a Filtered Autoregressive Model

Nicola Rebora Centro di Ricerca Interuniversitario in Monitoraggio Ambientale, University of Genoa, Savona, Italy

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Luca Ferraris Centro di Ricerca Interuniversitario in Monitoraggio Ambientale, University of Genoa, Savona, Italy

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Jost von Hardenberg Istituto di Scienze dell’Atmosfera e del Clima, CNR, Torino, and Centro di Ricerca Interuniversitario in Monitoraggio Ambientale, University of Genoa, Savona, Italy

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Antonello Provenzale Istituto di Scienze dell’Atmosfera e del Clima, CNR, Torino, and Centro di Ricerca Interuniversitario in Monitoraggio Ambientale, University of Genoa, Savona, Italy

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Print Publication:
01 Aug 2006
DOI:
https://doi.org/10.1175/JHM517.1
Page(s):
724–738
Restricted access

Abstract

A method is introduced for stochastic rainfall downscaling that can be easily applied to the precipitation forecasts provided by meteorological models. Our approach, called the Rainfall Filtered Autoregressive Model (RainFARM), is based on the nonlinear transformation of a Gaussian random field, and it conserves the information present in the rainfall fields at larger scales. The procedure is tested on two radar-measured intense rainfall events, one at midlatitude and the other in the Tropics, and it is shown that the synthetic fields generated by RainFARM have small-scale statistical properties that are consistent with those of the measured precipitation fields. The application of the disaggregation procedure to an example meteorological forecast illustrates how the method can be implemented in operational practice.

Corresponding author address: Nicola Rebora, Centro di Ricerca Interuniversitario in Monitoraggio Ambientale, University of Genoa, Via Cadorna 7, I-17100 Savona, Italy. Email: nicola@cima.unige.it

Abstract

A method is introduced for stochastic rainfall downscaling that can be easily applied to the precipitation forecasts provided by meteorological models. Our approach, called the Rainfall Filtered Autoregressive Model (RainFARM), is based on the nonlinear transformation of a Gaussian random field, and it conserves the information present in the rainfall fields at larger scales. The procedure is tested on two radar-measured intense rainfall events, one at midlatitude and the other in the Tropics, and it is shown that the synthetic fields generated by RainFARM have small-scale statistical properties that are consistent with those of the measured precipitation fields. The application of the disaggregation procedure to an example meteorological forecast illustrates how the method can be implemented in operational practice.

Corresponding author address: Nicola Rebora, Centro di Ricerca Interuniversitario in Monitoraggio Ambientale, University of Genoa, Via Cadorna 7, I-17100 Savona, Italy. Email: nicola@cima.unige.it

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