Open AccessRainFARM: Rainfall Downscaling by a Filtered Autoregressive Model
Author(s) -
Nicola Rebora,
Luca Ferraris,
Jost von Hardenberg,
A. Provenzale
Publication year - 2006
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm517.1
Subject(s) - downscaling , autoregressive model , precipitation , environmental science , meteorology , climatology , scale (ratio) , radar , computer science , mathematics , statistics , geology , geography , telecommunications , cartography
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