Articles | Volume 23, issue 1
Nonlin. Processes Geophys., 23, 13–20, 2016
https://doi.org/10.5194/npg-23-13-2016
Nonlin. Processes Geophys., 23, 13–20, 2016
https://doi.org/10.5194/npg-23-13-2016

Research article 27 Jan 2016

Research article | 27 Jan 2016

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

T. Soares dos Santos et al.

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Status: closed
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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Thalyta Santos on behalf of the Authors (01 Jan 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (07 Jan 2016) by Vicente Perez-Munuzuri
RR by Anonymous Referee #1 (07 Jan 2016)
ED: Publish as is (07 Jan 2016) by Vicente Perez-Munuzuri
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Short summary
Statistical downscaling is widely used in large operational centers around the world, using exclusively linear relations (MLR); this study uses a statistical downscaling methodology using a nonlinear technique known as ANNs with CMIP5 project data. The artificial neural network can perform tasks that a linear program cannot. The main advantages of this are its temporal processing ability and its ability to incorporate several preceding predictor values as input without any additional effort.