Improvements in precipitation simulation over South America for past and future climates via multi-model combination

Coutinho, Maytê Duarte Leal; Lima, Kellen Carla; Silva, Cláudio Moisés Santos e

Improvements in precipitation simulation over South America for past and future climates via multi-model combination

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dc.contributor.author	Coutinho, Maytê Duarte Leal
dc.contributor.author	Lima, Kellen Carla
dc.contributor.author	Silva, Cláudio Moisés Santos e
dc.date.accessioned	2020-09-18T14:29:05Z
dc.date.available	2020-09-18T14:29:05Z
dc.date.issued	2016-09-26
dc.description.resumo	Combining individual forecasts is one of the practices used to improve weather prediction results. Identifying which combination of techniques results in a more accurate forecast is the subject of many comparative studies as well proposals for combined methods. Here we compare three combination techniques: (1) principal component regression (PCR), (2) convex combination by mean squared errors (MSE) and (3) ensemble average to combine six regional climate models of the Regional Climate Change Assessment for the La Plata Basin Project (CLARIS-LPB) for variable rainfall in three regions: Amazon (AMZ), Northeastern Brazil (NEB) and La Plata Basin (LPB), for the past (1961–1990) and future (2071–2100) climates. The results indicate that the average RMSE values showed improved representation of climate for LPB in some months, which is an important advance in climate studies. On the other hand, PCR presented greater accuracy (lower RMSE) than MSE in the AMZ and NEB regions. In winter months, both combinations presented lower RMSE results, mainly PCR in the three study regions. The correlation coefficient supports the results already found, namely, PCR obtained moderate to strong correlations, which were statistically significant at 5 % in both regions for all months, while MSE presented low to moderate correlations, which were statically significant at 5 % only in some months. Based on that, PCR achieved the best corrected forecast, as it was superior in forecasting precipitation due to the lower RMSE value. It is noteworthy that the PCR data were first subjected to principal component analysis (PCA) and the scores were used to perform the prediction	pt_BR
dc.identifier.citation	COUTINHO, Maytê Duarte Leal; LIMA, Kellen Carla; SILVA, Cláudio Moisés Santos e. Improvements in precipitation simulation over South America for past and future climates via multi-model combination. Climate Dynamics, [S.L.], v. 49, n. 1-2, p. 343-361, 26 set. 2016. Disponível em: https://link.springer.com/article/10.1007%2Fs00382-016-3346-6. Acesso em: 10 ago. 2020. http://dx.doi.org/10.1007/s00382-016-3346-6.	pt_BR
dc.identifier.doi	10.1007/s00382-016-3346-6
dc.identifier.issn	0930-7575
dc.identifier.issn	1432-0894
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/30097
dc.language	en	pt_BR
dc.publisher	Springer	pt_BR
dc.rights	Attribution 3.0 Brazil	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/br/	*
dc.subject	Regional models	pt_BR
dc.subject	Principal component regression	pt_BR
dc.subject	Convex combination	pt_BR
dc.subject	Ensemble average	pt_BR
dc.subject	Outliers	pt_BR
dc.title	Improvements in precipitation simulation over South America for past and future climates via multi-model combination	pt_BR
dc.type	article	pt_BR