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| Titel |
Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America |
| VerfasserIn |
T. S. Santos, D. Mendes, R. R. Torres |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 4 ; Nr. 2, no. 4 (2015-08-06), S.1317-1337 |
| Datensatznummer |
250115191
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| Publikation (Nr.) |
 copernicus.org/npgd-2-1317-2015.pdf |
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| Zusammenfassung |
| 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 (ANN) 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 out- put and observed monthly precipitation. We used GCMs 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 ANN
significantly outperforms the MLR downscaling of monthly precipitation
variability. |
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