Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais

Detalhes bibliográficos
Autor(a) principal: Tenorio, Ricardo Bruno de Araújo
Data de Publicação: 2022
Outros Autores: Fernandez, José Henrique, Mendes, David, da Silva Júnior, José Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/40763
Resumo: O gelo marinho desempenha um papel fundamental na regulação térmica das regiões polares. Observações de satélites evidenciam que na Antártica o gelo apresentava, na série histórica, tendências positivas em cobertura e extensão. Em 2019 houve um padrão de inversões entre os valores da normal climatológica e dos dados de reanálise. Nesse contexto, este estudo teve como principal objetivo avaliar o potencial de previsibilidade de cobertura de gelo marinho com a aplicação de técnicas de RNAs em 3 mares que banham o continente Antártico, a saber: Weddell, Bellingshausen e Amundsen. Para tanto, foram utilizados como previsores a temperatura da superfície do mar, a temperatura do ar a 2 metros, a velocidade do vento a 10 metros, o albedo e os fluxos de calor latente e sensível, no período de 1979 a 2019. Os dados foram particionados em 70% para treinamento e 30% para testes. Modelos SARIMAX serviram como valores de referência para aferição da precisão das previsões com RNAs. Em todos os meses com anomalias absolutas superiores a 15% de concentração, o modelo de RNA CNN-LSTM superou os modelos MLP e SARIMAX.
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spelling Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais ArtificiaisAprendizado profundo; CNN-LSTM; TensorFlowO gelo marinho desempenha um papel fundamental na regulação térmica das regiões polares. Observações de satélites evidenciam que na Antártica o gelo apresentava, na série histórica, tendências positivas em cobertura e extensão. Em 2019 houve um padrão de inversões entre os valores da normal climatológica e dos dados de reanálise. Nesse contexto, este estudo teve como principal objetivo avaliar o potencial de previsibilidade de cobertura de gelo marinho com a aplicação de técnicas de RNAs em 3 mares que banham o continente Antártico, a saber: Weddell, Bellingshausen e Amundsen. Para tanto, foram utilizados como previsores a temperatura da superfície do mar, a temperatura do ar a 2 metros, a velocidade do vento a 10 metros, o albedo e os fluxos de calor latente e sensível, no período de 1979 a 2019. Os dados foram particionados em 70% para treinamento e 30% para testes. Modelos SARIMAX serviram como valores de referência para aferição da precisão das previsões com RNAs. Em todos os meses com anomalias absolutas superiores a 15% de concentração, o modelo de RNA CNN-LSTM superou os modelos MLP e SARIMAX.Universidade Federal do Rio de JaneiroCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Ciências ClimáticasTenorio, Ricardo Bruno de AraújoFernandez, José HenriqueMendes, Davidda Silva Júnior, José Pedro2022-06-21info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/4076310.11137/1982-3908_2022_45_40763Anuário do Instituto de Geociências; Vol 45 (2022)Anuário do Instituto de Geociências; Vol 45 (2022)1982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJenghttps://revistas.ufrj.br/index.php/aigeo/article/view/40763/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40763/15413https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40763/15854/*ref*/Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Ir-ving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Leven-berg, J., Mané, D., Monga, R., Moore S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, T., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. & Zheng, X. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. https://arxiv.org/pdf/1603.04467.pdf/*ref*/Akaike, H. 1974, ‘A new look at the statistical model identification’, IEEE Trans-actions on Automatic Control, vol. 19, no. 6, pp. 716-23. https://doi.org/10.1109/TAC.1974.1100705/*ref*/Armour, K.C., Scott, J., Donohoe, A., Newsom, E.R. & Marshall J.C. 2016, ‘Southern Ocean warming delayed by circumpolar upwelling and equa-torward transport’, Nature Geoscience, vol. 9, no. 7, pp. 549-54. https://doi.org/10.1038/ngeo2731/*ref*/Boetius, A., Anesio, A.M., Deming, J.W., Mikucki, J.A. & Rapp, J.Z. 2015, ‘Mi-crobial ecology of the cryosphere: sea ice and glacial habitats’, Nature Re-views Microbiology, vol. 13, no. 11, pp. 677-90. https://doi.org/10.1038/nrmicro3522/*ref*/Box, G.E.P. & Jenkins, G.M. 1976, Time Series Analysis: Forecasting and Con-trol, Holden-Day, San Francisco, CA./*ref*/Breiman, L. 2001, ‘Random forests’, Machine learning, vol. 45, no. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324/*ref*/Brownlee, J. 2016, Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras, Machine Learning Mastery./*ref*/Cavalieri, D.J., Gloersen P., Parkinson, C.L., Comiso, J.C. & Zwally, H.J. 1997, ‘Observed hemispheric asymmetry in global sea ice changes’, Science, vol. 278, no. 5340, pp. 1104-6. https://doi.org/10.1126/science.278.5340.1104/*ref*/Cavalieri, D. & Parkinson, C.L. 2012, ‘Arctic sea ice variability and trends, 1979-2010’, The Cryosphere, vol. 6, no. 4, pp. 881-9. https://doi.org/10.5194/tc-6-881-2012/*ref*/Chambault, P., Albertsen, C.M., Patterson, T.A., Hansen, R.G., Tervo, O., Laidre, K.L. & Heide-Jørgensen, M.P. 2018, ‘Sea surface temperature pre-dicts the movements of the Arctic cetacean: the bowhead whale’, Scientific reports, vol. 8, no. 1, pp. 1-12. https://doi.org/10.1038/s41598-018-27966-1/*ref*/Chemke, R. & Polvani, L.M. 2020, ‘Using multiple large ensembles to elucidate the discrepancy between the 1979-2019 modeled and observed Antarctic Sea ice trends’, Geophysical Research Letters, vol. 47, no. 15, pp. e2020GL088339. https://doi.org/10.1029/2020GL088339/*ref*/Chen, J., Li, M. & Wang, W. 2012, ‘Statistical uncertainty estimation using Ran-dom Forests and its application to drought forecast’, Mathematical Prob-lems in Engineering, vol. 2012, no. 915053. https://doi.org/10.1155/2012/915053/*ref*/Evermann, J., Rehse, J.R. & Fettke, P. 2017, XES Tensorflow: Process predic-tion using the Tensorflow deep-learning framework, ArXv, vol. 1. https://arxiv.org/pdf/1705.01507.pdf/*ref*/Gagné, M., Gillett, N. & Fyfe, P. 2015, ‘Observed and simulated changes in Antarctic sea ice extent over the past 50 years’, Geophysical Research Let-ters, vol. 42, no. 1, pp. 90-5. https://doi.org/10.1002/2014GL062231/*ref*/Gloersen, P., Campbell, W.J., Cavalieri, D.J., Comiso, J.C., Parkinson, C.L. & Zwally, H.J. 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Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2022-12-28T20:46:28Zoai:www.revistas.ufrj.br:article/40763Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2022-12-28T20:46:28Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
title Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
spellingShingle Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
Tenorio, Ricardo Bruno de Araújo
Aprendizado profundo; CNN-LSTM; TensorFlow
title_short Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
title_full Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
title_fullStr Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
title_full_unstemmed Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
title_sort Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
author Tenorio, Ricardo Bruno de Araújo
author_facet Tenorio, Ricardo Bruno de Araújo
Fernandez, José Henrique
Mendes, David
da Silva Júnior, José Pedro
author_role author
author2 Fernandez, José Henrique
Mendes, David
da Silva Júnior, José Pedro
author2_role author
author
author
dc.contributor.none.fl_str_mv Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Ciências Climáticas
dc.contributor.author.fl_str_mv Tenorio, Ricardo Bruno de Araújo
Fernandez, José Henrique
Mendes, David
da Silva Júnior, José Pedro
dc.subject.por.fl_str_mv Aprendizado profundo; CNN-LSTM; TensorFlow
topic Aprendizado profundo; CNN-LSTM; TensorFlow
description O gelo marinho desempenha um papel fundamental na regulação térmica das regiões polares. Observações de satélites evidenciam que na Antártica o gelo apresentava, na série histórica, tendências positivas em cobertura e extensão. Em 2019 houve um padrão de inversões entre os valores da normal climatológica e dos dados de reanálise. Nesse contexto, este estudo teve como principal objetivo avaliar o potencial de previsibilidade de cobertura de gelo marinho com a aplicação de técnicas de RNAs em 3 mares que banham o continente Antártico, a saber: Weddell, Bellingshausen e Amundsen. Para tanto, foram utilizados como previsores a temperatura da superfície do mar, a temperatura do ar a 2 metros, a velocidade do vento a 10 metros, o albedo e os fluxos de calor latente e sensível, no período de 1979 a 2019. Os dados foram particionados em 70% para treinamento e 30% para testes. Modelos SARIMAX serviram como valores de referência para aferição da precisão das previsões com RNAs. Em todos os meses com anomalias absolutas superiores a 15% de concentração, o modelo de RNA CNN-LSTM superou os modelos MLP e SARIMAX.
publishDate 2022
dc.date.none.fl_str_mv 2022-06-21
dc.type.none.fl_str_mv

dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/40763
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url https://revistas.ufrj.br/index.php/aigeo/article/view/40763
identifier_str_mv 10.11137/1982-3908_2022_45_40763
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/40763/pdf
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/*ref*/Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Ir-ving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Leven-berg, J., Mané, D., Monga, R., Moore S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, T., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. & Zheng, X. 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. https://arxiv.org/pdf/1603.04467.pdf
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/*ref*/Armour, K.C., Scott, J., Donohoe, A., Newsom, E.R. & Marshall J.C. 2016, ‘Southern Ocean warming delayed by circumpolar upwelling and equa-torward transport’, Nature Geoscience, vol. 9, no. 7, pp. 549-54. https://doi.org/10.1038/ngeo2731
/*ref*/Boetius, A., Anesio, A.M., Deming, J.W., Mikucki, J.A. & Rapp, J.Z. 2015, ‘Mi-crobial ecology of the cryosphere: sea ice and glacial habitats’, Nature Re-views Microbiology, vol. 13, no. 11, pp. 677-90. https://doi.org/10.1038/nrmicro3522
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/*ref*/Breiman, L. 2001, ‘Random forests’, Machine learning, vol. 45, no. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324
/*ref*/Brownlee, J. 2016, Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras, Machine Learning Mastery.
/*ref*/Cavalieri, D.J., Gloersen P., Parkinson, C.L., Comiso, J.C. & Zwally, H.J. 1997, ‘Observed hemispheric asymmetry in global sea ice changes’, Science, vol. 278, no. 5340, pp. 1104-6. https://doi.org/10.1126/science.278.5340.1104
/*ref*/Cavalieri, D. & Parkinson, C.L. 2012, ‘Arctic sea ice variability and trends, 1979-2010’, The Cryosphere, vol. 6, no. 4, pp. 881-9. https://doi.org/10.5194/tc-6-881-2012
/*ref*/Chambault, P., Albertsen, C.M., Patterson, T.A., Hansen, R.G., Tervo, O., Laidre, K.L. & Heide-Jørgensen, M.P. 2018, ‘Sea surface temperature pre-dicts the movements of the Arctic cetacean: the bowhead whale’, Scientific reports, vol. 8, no. 1, pp. 1-12. https://doi.org/10.1038/s41598-018-27966-1
/*ref*/Chemke, R. & Polvani, L.M. 2020, ‘Using multiple large ensembles to elucidate the discrepancy between the 1979-2019 modeled and observed Antarctic Sea ice trends’, Geophysical Research Letters, vol. 47, no. 15, pp. e2020GL088339. https://doi.org/10.1029/2020GL088339
/*ref*/Chen, J., Li, M. & Wang, W. 2012, ‘Statistical uncertainty estimation using Ran-dom Forests and its application to drought forecast’, Mathematical Prob-lems in Engineering, vol. 2012, no. 915053. https://doi.org/10.1155/2012/915053
/*ref*/Evermann, J., Rehse, J.R. & Fettke, P. 2017, XES Tensorflow: Process predic-tion using the Tensorflow deep-learning framework, ArXv, vol. 1. https://arxiv.org/pdf/1705.01507.pdf
/*ref*/Gagné, M., Gillett, N. & Fyfe, P. 2015, ‘Observed and simulated changes in Antarctic sea ice extent over the past 50 years’, Geophysical Research Let-ters, vol. 42, no. 1, pp. 90-5. https://doi.org/10.1002/2014GL062231
/*ref*/Gloersen, P., Campbell, W.J., Cavalieri, D.J., Comiso, J.C., Parkinson, C.L. & Zwally, H.J. (eds) 1992, Arctic and antarctic sea ice: satellite passive-microwave observations and analysis, NASA, Washington D.C. Haykin, S. 2007, Redes Neurais: Princípios e Prática, 2nd edn, Bookman, Porto Alegre, RS.
/*ref*/Ho, T.K. 1995, ‘Random decision forests’, 3rd international conference on doc-ument analysis and recognition 1995, IEEE, Murray Hill, NJ, pp. 278-82. https://doi.org/10.1109/ICDAR.1995.598994
/*ref*/Hochreiter, S. & Schmidhuber, J. 1997, ‘Long Short-Term Memory’, Neural computation, vol. 9, no. 8, pp. 1735-80. https://doi.org/10.1162/neco.1997.9.8.1735
/*ref*/Hutchinson, D.K., England, M.H., Santoso, A. & Hogg, A.M. 2013, ‘Interhe-mispheric asymmetry in transient global warming: The role of Drake Pas-sage’, Geophysical Research Letters, vol. 40, no. 8, pp. 1587-93. https://doi.org/10.1002/grl.50341
/*ref*/Hu, M.Y., Zhang, G., Jiang, C.X. & Patuwo, B.E. 1999, ‘A Cross-Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting’, Decision Sciences, vol. 30, no. 1, pp. 197-216. https://doi.org/10.1111/j.1540-5915.1999.tb01606.x
/*ref*/Kirchmeier-Young, M.C., Zwiers, F.W. & Gillett, N.P. 2017, ‘Attribution of ex-treme events in Arctic sea ice extent’, Journal of Climate, vol. 30, no. 2, pp. 553-71. https://doi.org/10.1175/JCLI-D-16-0412.1
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dc.source.none.fl_str_mv Anuário do Instituto de Geociências; Vol 45 (2022)
Anuário do Instituto de Geociências; Vol 45 (2022)
1982-3908
0101-9759
reponame:Anuário do Instituto de Geociências (Online)
instname:Universidade Federal do Rio de Janeiro (UFRJ)
instacron:UFRJ
instname_str Universidade Federal do Rio de Janeiro (UFRJ)
instacron_str UFRJ
institution UFRJ
reponame_str Anuário do Instituto de Geociências (Online)
collection Anuário do Instituto de Geociências (Online)
repository.name.fl_str_mv Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)
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