Statistical prediction of winter rainfall in patagonia (Argentina)

This chapter describes rainfall evolution in southern Argentina (Patagonia) and faces with the possibility to predict winter rainfall using oceanic and atmospheric predictors. The Andes Mountain ranges along the west of Patagonia and a large plateau extends towards the east. A decrease of rainfall w...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: González, M.H., Herrera, N.
Formato: CHAP
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_97816332_v11_n_p221_Gonzalez
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_97816332_v11_n_p221_Gonzalez
record_format dspace
spelling todo:paper_97816332_v11_n_p221_Gonzalez2023-10-03T16:44:34Z Statistical prediction of winter rainfall in patagonia (Argentina) González, M.H. Herrera, N. This chapter describes rainfall evolution in southern Argentina (Patagonia) and faces with the possibility to predict winter rainfall using oceanic and atmospheric predictors. The Andes Mountain ranges along the west of Patagonia and a large plateau extends towards the east. A decrease of rainfall was observed in all seasons in central and east Patagonia meanwhile an increment is observed in southern part of Los Andes. The study area was regionalized using a principal component analysis and four regions could be defined. Almost all regions showed an annual cycle with maximum rainfall in autumnwinter. Therefore, several different statistical models: an autoregressive integrated moving average model (ARIMA), Holt Winter (HW), Climate Prediction Tool (CPT) and an ensemble of all, called multimodel were applied to predict MJJ (May to July) Patagonia rainfall in four regions defined in Patagonia. They were designed with MJJ rainfall for the period 1975-2007 in CPT model and for the period 1988-2007 in ARIMA and Holt Winter because this last two models use 1975-1987 values to generate the final scheme. All the models were proved for 2008-2012. The main result of this work is that the consideration of ARIMA and HW in the multimodel ensemble improves the efficiency obtained using CPT. This result was supported by the calculation of efficiency coefficients which represent the ability to detect over and sub normal rainfall. © 2014 by Nova Science Publishers, Inc. All rights reserved. Fil:González, M.H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CHAP info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97816332_v11_n_p221_Gonzalez
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
description This chapter describes rainfall evolution in southern Argentina (Patagonia) and faces with the possibility to predict winter rainfall using oceanic and atmospheric predictors. The Andes Mountain ranges along the west of Patagonia and a large plateau extends towards the east. A decrease of rainfall was observed in all seasons in central and east Patagonia meanwhile an increment is observed in southern part of Los Andes. The study area was regionalized using a principal component analysis and four regions could be defined. Almost all regions showed an annual cycle with maximum rainfall in autumnwinter. Therefore, several different statistical models: an autoregressive integrated moving average model (ARIMA), Holt Winter (HW), Climate Prediction Tool (CPT) and an ensemble of all, called multimodel were applied to predict MJJ (May to July) Patagonia rainfall in four regions defined in Patagonia. They were designed with MJJ rainfall for the period 1975-2007 in CPT model and for the period 1988-2007 in ARIMA and Holt Winter because this last two models use 1975-1987 values to generate the final scheme. All the models were proved for 2008-2012. The main result of this work is that the consideration of ARIMA and HW in the multimodel ensemble improves the efficiency obtained using CPT. This result was supported by the calculation of efficiency coefficients which represent the ability to detect over and sub normal rainfall. © 2014 by Nova Science Publishers, Inc. All rights reserved.
format CHAP
author González, M.H.
Herrera, N.
spellingShingle González, M.H.
Herrera, N.
Statistical prediction of winter rainfall in patagonia (Argentina)
author_facet González, M.H.
Herrera, N.
author_sort González, M.H.
title Statistical prediction of winter rainfall in patagonia (Argentina)
title_short Statistical prediction of winter rainfall in patagonia (Argentina)
title_full Statistical prediction of winter rainfall in patagonia (Argentina)
title_fullStr Statistical prediction of winter rainfall in patagonia (Argentina)
title_full_unstemmed Statistical prediction of winter rainfall in patagonia (Argentina)
title_sort statistical prediction of winter rainfall in patagonia (argentina)
url http://hdl.handle.net/20.500.12110/paper_97816332_v11_n_p221_Gonzalez
work_keys_str_mv AT gonzalezmh statisticalpredictionofwinterrainfallinpatagoniaargentina
AT herreran statisticalpredictionofwinterrainfallinpatagoniaargentina
_version_ 1782028883102531584

Ejemplares similares