Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America
Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South A...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-01-01
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| Series: | Nonlinear Processes in Geophysics |
| Online Access: | http://www.nonlin-processes-geophys.net/23/13/2016/npg-23-13-2016.pdf |
| Summary: | Several studies have been devoted to dynamic and statistical downscaling for
analysis of both climate variability and climate change. This paper
introduces an application of artificial neural networks (ANNs) and multiple
linear regression (MLR) by principal components to estimate rainfall in South
America. This method is proposed for downscaling monthly precipitation time
series over South America for three regions: the Amazon; northeastern Brazil;
and the La Plata Basin, which is one of the regions of the planet that will
be most affected by the climate change projected for the end of the 21st
century. The downscaling models were developed and validated using CMIP5
model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970–1999) and two scenarios (RCP 2.6 and 8.5; 2070–2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability. |
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| ISSN: | 1023-5809 1607-7946 |