Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais
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 climatol...
| Published in: | Anuário do Instituto de Geociências |
|---|---|
| Main Authors: | , , , |
| Other Authors: | , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Universidade Federal do Rio de Janeiro
2022
|
| Subjects: | |
| Online Access: | https://revistas.ufrj.br/index.php/aigeo/article/view/40763 https://doi.org/10.11137/1982-3908_2022_45_40763 |
| id |
ftufriodejaneiro:oai:www.revistas.ufrj.br:article/40763 |
|---|---|
| record_format |
openpolar |
| institution |
Open Polar |
| collection |
Portal de Periódicos da UFRJ (Universidade Federal do Rio de Janeiro) |
| op_collection_id |
ftufriodejaneiro |
| language |
English |
| topic |
Aprendizado profundo CNN-LSTM TensorFlow |
| spellingShingle |
Aprendizado profundo CNN-LSTM TensorFlow Tenorio, Ricardo Bruno de Araújo Fernandez, José Henrique Mendes, David da Silva Júnior, José Pedro Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais |
| topic_facet |
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. |
| author2 |
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Ciências Climáticas |
| format |
Article in Journal/Newspaper |
| author |
Tenorio, Ricardo Bruno de Araújo Fernandez, José Henrique Mendes, David da Silva Júnior, José Pedro |
| author_facet |
Tenorio, Ricardo Bruno de Araújo Fernandez, José Henrique Mendes, David da Silva Júnior, José Pedro |
| author_sort |
Tenorio, Ricardo Bruno de Araújo |
| title |
Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais |
| 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 |
| publisher |
Universidade Federal do Rio de Janeiro |
| publishDate |
2022 |
| url |
https://revistas.ufrj.br/index.php/aigeo/article/view/40763 https://doi.org/10.11137/1982-3908_2022_45_40763 |
| geographic |
Weddell |
| geographic_facet |
Weddell |
| genre |
Annals of Glaciology Antártica Arctic The Cryosphere |
| genre_facet |
Annals of Glaciology Antártica Arctic The Cryosphere |
| op_source |
Anuário do Instituto de Geociências; v. 45 (2022) 1982-3908 0101-9759 |
| op_relation |
https://revistas.ufrj.br/index.php/aigeo/article/view/40763/pdf https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40763/15413 https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40763/15854 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 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 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 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 Box, G.E.P. & Jenkins, G.M. 1976, Time Series Analysis: Forecasting and Con-trol, Holden-Day, San Francisco, CA. Breiman, L. 2001, ‘Random forests’, Machine learning, vol. 45, no. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324 Brownlee, J. 2016, Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras, Machine Learning Mastery. 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 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 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 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 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 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 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 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. 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 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 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 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 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 Lawrence, S., Giles, C.L., Tsoi, A.C. & Back, A.D. 1997, ‘Face recognition: A convolutional neural-network approach’, IEEE transactions on neural net-works, vol. 8, no. 1, pp. 98-113. https://doi.org/10.1109/72.554195 LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. 1998, ‘Gradient-based learning applied to document recognition’, Proceedings of the IEEE, vol. 86, no. 21, pp. 2278-324. https://doi.org/10.1109/5.726791 Meehl, G.A, Arblaster, J.M., Chung, C.T.Y., Holland, M.M., DuVivier, A., Thompson, L., Yang, D. & Bitz, C.M. 2019, ‘Sustained ocean changes con-tributed to sudden Antarctic ice retreat in late 2016’, Nature Communica-tions, vol. 10, no. 1, pp. 1-9. https://doi.org/10.1038/s41467-018-07865-9 Olah, C. 2015, Understanding LSTM Networks, Colah’s Blog, weblog, viewed 13 July 2021, <https://colah.github.io/posts/2015-08-Understanding-LSTMs/> Oliva, M., Navarro, F., Hrbáček, F., Hernández, A., Nývlt, D., Pereira, P., Ruiz-Fernández, J. & Trigo, R. 2017, ‘Recent regional climate cooling on the Antarctic Peninsula and associated impacts on the cryosphere’, Science of The Total Environment, vol. 580, pp. 210-23. https://doi.org/10.1016/j.scitotenv.2016.12.030 Parkinson, C.L. 2002, ‘Trends in the length of the Southern Ocean sea-ice sea-son’, Annals of Glaciology, vol. 34, no. 1, pp. 435-40. https://doi.org/10.3189/172756402781817482 Parkinson, C.L. 2019, ’A 40-y record reveals gradual Antarctic Sea ice increas-es followed by decreases at rates far exceeding the rates seen in the Arc-tic’, Proceedings of the National Academy of Sciences, vol. 116, no. 29, pp. 14414-23. https://doi.org/10.1073/pnas.1906556116 Raphael, M.N., Marshall, G.J., Turner, J., Fogt, R.L., Schneider, D., Dixon, D.A., Hosking, J.S., Jones, J.M. & Hobbs, W.R. 2016, ‘The Amundsen Sea Low: Variability, Change and Impact on Antarctic climate’, Bulletin of the Ameri-can Meteorological Society, vol. 97, no. 1, pp. 111-21. https://doi.org/10.1175/BAMS-D-14-00018.1 Rosenblatt, F. 1958, ‘The Perceptron: A probabilistic model for information stor-age and organization in the brain’, Psychological Review, vol. 65, no. 6, pp. 386-408. https://doi.apa.org/doi/10.1037/h0042519 Rouch, L.A., Dörr, J., Holmes, C.R., Massonnet, F., Blockley, E.W., Notz, D., Rackwow, T., Raphael, M.N., O’Farrell, S.P., Bailey, D.A. & Bitz, C.M. 2020, ‘Antarctic Sea ice area in CMIP6’, Geophysical Research Letters, vol. 47, no. 9, pp. e2019GL086729. https://doi.org/10.1029/2019GL086729 Serreze, M.C., Holland, M.M. & Stroeve, J. 2007, ‘Perspectives on the Artic’s Shrinking sea-ice cover’, Science, vol. 315, no. 5818, pp. 1533-36. https://doi.org/10.1126/science.1139426 Strobl, C., Boulesteix, A.L., Zeileis, A., & Hothorn, T. 2007, ‘Bias in random for-est variable importance measures: Illustrations, sources and a solution’, BMC bioinformatics, vol. 8, no. 1, pp. 1-21. https://doi.org/10.1186/1471-2105-8-25 Stroeve, J.C., Kattsov, V., Barret A., Serreze, M., Pavlova, T., Holland, M. & Meier, W. 2012, ‘Trends in Arctic Sea ice extent from CMIP5, CMIP3 and observations’, Geophysical Research Letters, vol. 39, no. 16, pp. L16502. https://doi.org/10.1029/2012GL052676 Turner, J., Lu, H., White, I., King, J.C., Phillips, T., Scott H.J., Bracegirdle, T.J., Marshall, G.J., Mulvaney, R. & Deb, P. 2016, ‘Absence of 21st century warming on Antarctic Peninsula consistent with natural variability’, Nature, vol. 535, no. 7612, pp. 411-5. https://doi.org/10.1038/nature18645 Turner, J., Guarino, M.V., Arnatt, J., Jena, B., Marshall, G.J., Phillips, T., Bajish, C.C., Clem, K., Wang, Z., Andersson, T., Murphy, E.J. & Cavanagh, R. 2020, ‘Recent Decrease of Summer Sea Ice in the Weddell Sea, Antarcti-ca’, Geophysical Research Letters, vol. 47, no. 11, pp. e2020GL087127. https://doi.org/10.1029/2020GL087127 Yegnanarayana, B. 2005, Artificial Neural Networks, Prentice-Hall of India, No-va Deli. Young, T., Hazarika, D., Poria, S. & Cambria, E. 2018, ‘Recent trends in deep learning based natural language processing’, IEEE Computational intelli-gence magazine, vol. 13, no. 3, pp. 55-75. https://arxiv.org/abs/1708.02709 Vaughan, D.G., Marshall, G.J., Connolley, W.M., Parkinson, C., Mulvaney, R., Hodgson, D.A., King, J.C., Pudsey, C.J. & Turner, J. 2003, ‘Recent rapid regional climate warming on the Antarctic Peninsula’, Climate Change, vol. 60, no. 3, pp. 243-74. https://doi.org/10.1023/A:1026021217991 Wilks, D.S. 2006, Statistical Methods in the Atmospheric Sciences, 2nd edn, Academic Press, London. https://revistas.ufrj.br/index.php/aigeo/article/view/40763 doi:10.11137/1982-3908_2022_45_40763 |
| op_rights |
Direitos autorais 2022 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 |
| op_rightsnorm |
CC-BY |
| op_doi |
https://doi.org/10.11137/1982-3908_2022_45_40763 https://doi.org/10.1109/TAC.1974.1100705 https://doi.org/10.1023/A:1010933404324 https://doi.org/10.1109/ICDAR.1995.598994 https://doi.org/10.3189/172756402781817482 https://doi.org/10.1073/pnas.1 |
| container_title |
Anuário do Instituto de Geociências |
| container_volume |
45 |
| _version_ |
1766003892259127296 |
| spelling |
ftufriodejaneiro:oai:www.revistas.ufrj.br:article/40763 2023-05-15T13:29:52+02:00 Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais Tenorio, Ricardo Bruno de Araújo Fernandez, José Henrique Mendes, David da Silva Júnior, José Pedro Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Universidade Federal do Rio Grande do Norte, Programa de Pós-Graduação em Ciências Climáticas 2022-06-21 application/pdf https://revistas.ufrj.br/index.php/aigeo/article/view/40763 https://doi.org/10.11137/1982-3908_2022_45_40763 eng eng Universidade Federal do Rio de Janeiro https://revistas.ufrj.br/index.php/aigeo/article/view/40763/pdf https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40763/15413 https://revistas.ufrj.br/index.php/aigeo/article/downloadSuppFile/40763/15854 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 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 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 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 Box, G.E.P. & Jenkins, G.M. 1976, Time Series Analysis: Forecasting and Con-trol, Holden-Day, San Francisco, CA. Breiman, L. 2001, ‘Random forests’, Machine learning, vol. 45, no. 1, pp. 5-32. https://doi.org/10.1023/A:1010933404324 Brownlee, J. 2016, Deep Learning with Python: Develop Deep Learning Models on Theano and TensorFlow using Keras, Machine Learning Mastery. 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 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 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 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 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 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 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 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. 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 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 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 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 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 Lawrence, S., Giles, C.L., Tsoi, A.C. & Back, A.D. 1997, ‘Face recognition: A convolutional neural-network approach’, IEEE transactions on neural net-works, vol. 8, no. 1, pp. 98-113. https://doi.org/10.1109/72.554195 LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. 1998, ‘Gradient-based learning applied to document recognition’, Proceedings of the IEEE, vol. 86, no. 21, pp. 2278-324. https://doi.org/10.1109/5.726791 Meehl, G.A, Arblaster, J.M., Chung, C.T.Y., Holland, M.M., DuVivier, A., Thompson, L., Yang, D. & Bitz, C.M. 2019, ‘Sustained ocean changes con-tributed to sudden Antarctic ice retreat in late 2016’, Nature Communica-tions, vol. 10, no. 1, pp. 1-9. https://doi.org/10.1038/s41467-018-07865-9 Olah, C. 2015, Understanding LSTM Networks, Colah’s Blog, weblog, viewed 13 July 2021, <https://colah.github.io/posts/2015-08-Understanding-LSTMs/> Oliva, M., Navarro, F., Hrbáček, F., Hernández, A., Nývlt, D., Pereira, P., Ruiz-Fernández, J. & Trigo, R. 2017, ‘Recent regional climate cooling on the Antarctic Peninsula and associated impacts on the cryosphere’, Science of The Total Environment, vol. 580, pp. 210-23. https://doi.org/10.1016/j.scitotenv.2016.12.030 Parkinson, C.L. 2002, ‘Trends in the length of the Southern Ocean sea-ice sea-son’, Annals of Glaciology, vol. 34, no. 1, pp. 435-40. https://doi.org/10.3189/172756402781817482 Parkinson, C.L. 2019, ’A 40-y record reveals gradual Antarctic Sea ice increas-es followed by decreases at rates far exceeding the rates seen in the Arc-tic’, Proceedings of the National Academy of Sciences, vol. 116, no. 29, pp. 14414-23. https://doi.org/10.1073/pnas.1906556116 Raphael, M.N., Marshall, G.J., Turner, J., Fogt, R.L., Schneider, D., Dixon, D.A., Hosking, J.S., Jones, J.M. & Hobbs, W.R. 2016, ‘The Amundsen Sea Low: Variability, Change and Impact on Antarctic climate’, Bulletin of the Ameri-can Meteorological Society, vol. 97, no. 1, pp. 111-21. https://doi.org/10.1175/BAMS-D-14-00018.1 Rosenblatt, F. 1958, ‘The Perceptron: A probabilistic model for information stor-age and organization in the brain’, Psychological Review, vol. 65, no. 6, pp. 386-408. https://doi.apa.org/doi/10.1037/h0042519 Rouch, L.A., Dörr, J., Holmes, C.R., Massonnet, F., Blockley, E.W., Notz, D., Rackwow, T., Raphael, M.N., O’Farrell, S.P., Bailey, D.A. & Bitz, C.M. 2020, ‘Antarctic Sea ice area in CMIP6’, Geophysical Research Letters, vol. 47, no. 9, pp. e2019GL086729. https://doi.org/10.1029/2019GL086729 Serreze, M.C., Holland, M.M. & Stroeve, J. 2007, ‘Perspectives on the Artic’s Shrinking sea-ice cover’, Science, vol. 315, no. 5818, pp. 1533-36. https://doi.org/10.1126/science.1139426 Strobl, C., Boulesteix, A.L., Zeileis, A., & Hothorn, T. 2007, ‘Bias in random for-est variable importance measures: Illustrations, sources and a solution’, BMC bioinformatics, vol. 8, no. 1, pp. 1-21. https://doi.org/10.1186/1471-2105-8-25 Stroeve, J.C., Kattsov, V., Barret A., Serreze, M., Pavlova, T., Holland, M. & Meier, W. 2012, ‘Trends in Arctic Sea ice extent from CMIP5, CMIP3 and observations’, Geophysical Research Letters, vol. 39, no. 16, pp. L16502. https://doi.org/10.1029/2012GL052676 Turner, J., Lu, H., White, I., King, J.C., Phillips, T., Scott H.J., Bracegirdle, T.J., Marshall, G.J., Mulvaney, R. & Deb, P. 2016, ‘Absence of 21st century warming on Antarctic Peninsula consistent with natural variability’, Nature, vol. 535, no. 7612, pp. 411-5. https://doi.org/10.1038/nature18645 Turner, J., Guarino, M.V., Arnatt, J., Jena, B., Marshall, G.J., Phillips, T., Bajish, C.C., Clem, K., Wang, Z., Andersson, T., Murphy, E.J. & Cavanagh, R. 2020, ‘Recent Decrease of Summer Sea Ice in the Weddell Sea, Antarcti-ca’, Geophysical Research Letters, vol. 47, no. 11, pp. e2020GL087127. https://doi.org/10.1029/2020GL087127 Yegnanarayana, B. 2005, Artificial Neural Networks, Prentice-Hall of India, No-va Deli. Young, T., Hazarika, D., Poria, S. & Cambria, E. 2018, ‘Recent trends in deep learning based natural language processing’, IEEE Computational intelli-gence magazine, vol. 13, no. 3, pp. 55-75. https://arxiv.org/abs/1708.02709 Vaughan, D.G., Marshall, G.J., Connolley, W.M., Parkinson, C., Mulvaney, R., Hodgson, D.A., King, J.C., Pudsey, C.J. & Turner, J. 2003, ‘Recent rapid regional climate warming on the Antarctic Peninsula’, Climate Change, vol. 60, no. 3, pp. 243-74. https://doi.org/10.1023/A:1026021217991 Wilks, D.S. 2006, Statistical Methods in the Atmospheric Sciences, 2nd edn, Academic Press, London. https://revistas.ufrj.br/index.php/aigeo/article/view/40763 doi:10.11137/1982-3908_2022_45_40763 Direitos autorais 2022 Anuário do Instituto de Geociências http://creativecommons.org/licenses/by/4.0 CC-BY Anuário do Instituto de Geociências; v. 45 (2022) 1982-3908 0101-9759 Aprendizado profundo CNN-LSTM TensorFlow info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2022 ftufriodejaneiro https://doi.org/10.11137/1982-3908_2022_45_40763 https://doi.org/10.1109/TAC.1974.1100705 https://doi.org/10.1023/A:1010933404324 https://doi.org/10.1109/ICDAR.1995.598994 https://doi.org/10.3189/172756402781817482 https://doi.org/10.1073/pnas.1 2023-01-01T02:19:41Z 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. Article in Journal/Newspaper Annals of Glaciology Antártica Arctic The Cryosphere Portal de Periódicos da UFRJ (Universidade Federal do Rio de Janeiro) Weddell Anuário do Instituto de Geociências 45 |