An R package to access climate change data for South America regionalized by the Eta Model of CPTEC/INPE
Resumo
Este artigo apresenta um pacote R desenvolvido com o objetivo de facilitar o acesso de dados de mudanças climáticas para a América do Sul gerados pelo CPTEC/INPE. São apresentados alguns trabalhos relacionados que foram utilizados como base no desenvolvimento do pacote. O pacote possui funções para acesso a dados climáticos e funções que auxiliam na utilização das funções do próprio pacote. Com este pacote, desenvolvedores e usuários da linguagem R podem contar com uma ferramenta de fácil utilização para o acesso e a manipulação destes dados.
Referências
Chou, S. C., et al. (2012) “Downscaling of South America Present Climate Driven by 4-Member HadCM3 Runs”, Climate Dynamics, 38: 635-653. DOI: 10.1007/s00382-011-1002-8.
Chou, S. C., et al. (2014) “Evaluation of the Eta Simulations Nested in Three Global Climate Models”, American Journal of Climate Change, 3, 438-454. DOI: 10.4236/ajcc.2014.35039.
Chou, S. C., et al. (2014a) “Assessment of Climate Change over South America under RCP 4.5 and 8.5 Downscaling Scenarios”, American Journal of Climate Change, 3, 512-527. DOI: 10.4236/ajcc.2014.35043.
Fraley, C., Raftery, A. E. and Gneiting, T. (2011) “Probabilistic weather forecasting in R”, The R Journal, 3(June), 55–63. DOI: 10.1198/jasa.2009.ap07184.
Lyra, A., et al. (2017). “Climate change projections over three metropolitan regions in Southeast Brazil using the non-hydrostatic Eta regional climate model at 5-km resolution”, Theor Appl Climatol. DOI: 10.1007/s00704-017-2067-z.
Marengo, J. A., et al. (2012) “Development of regional future climate change scenarios in South America using the Eta CPTEC/HadCM3 climate change projections: climatology and regional analyses for the Amazon, São Francisco and the Parana River Basins”, Climate Dynamics, 38: 1829-1848. DOI: 10.1007/s00382-011-1155-5.
Pesquero, J. F., et al. (2010). “Climate downscaling over South America for 1961-1970 using the Eta Model”, Theor Appl Climatol, 99: 75-93. DOI: 10.1007/s00704-009-0123-z.
Serrano-Notivoli, R., de Luis, M. and Beguería, S. (2017), “An R package for daily precipitation climate series reconstruction”, Environmental Modelling and Software, 89, 190–195. DOI: 10.1016/j.envsoft.2016.11.005.
Chou, S. C., et al. (2014) “Evaluation of the Eta Simulations Nested in Three Global Climate Models”, American Journal of Climate Change, 3, 438-454. DOI: 10.4236/ajcc.2014.35039.
Chou, S. C., et al. (2014a) “Assessment of Climate Change over South America under RCP 4.5 and 8.5 Downscaling Scenarios”, American Journal of Climate Change, 3, 512-527. DOI: 10.4236/ajcc.2014.35043.
Fraley, C., Raftery, A. E. and Gneiting, T. (2011) “Probabilistic weather forecasting in R”, The R Journal, 3(June), 55–63. DOI: 10.1198/jasa.2009.ap07184.
Lyra, A., et al. (2017). “Climate change projections over three metropolitan regions in Southeast Brazil using the non-hydrostatic Eta regional climate model at 5-km resolution”, Theor Appl Climatol. DOI: 10.1007/s00704-017-2067-z.
Marengo, J. A., et al. (2012) “Development of regional future climate change scenarios in South America using the Eta CPTEC/HadCM3 climate change projections: climatology and regional analyses for the Amazon, São Francisco and the Parana River Basins”, Climate Dynamics, 38: 1829-1848. DOI: 10.1007/s00382-011-1155-5.
Pesquero, J. F., et al. (2010). “Climate downscaling over South America for 1961-1970 using the Eta Model”, Theor Appl Climatol, 99: 75-93. DOI: 10.1007/s00704-009-0123-z.
Serrano-Notivoli, R., de Luis, M. and Beguería, S. (2017), “An R package for daily precipitation climate series reconstruction”, Environmental Modelling and Software, 89, 190–195. DOI: 10.1016/j.envsoft.2016.11.005.
Publicado
26/06/2018
Como Citar
DEZORDI, Marina Lopes; HÖLBIG, Carlos Amaral; PAVAN, Willingthon; FERNANDES, José Maurício Cunha.
An R package to access climate change data for South America regionalized by the Eta Model of CPTEC/INPE. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 9. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2018.2934.
