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Authors
# Name
1 Marcos Lage(mlage@ic.uff.br)
2 Fabio Victorino(fabiovict95@gmail.com)
3 Gustavo Muller(gustavomuller@id.uff.br)
4 Bruno Sá(bcunha@id.uff.br)
5 Aline Paes(alinepaes@ic.uff.br)
6 Annie Amorim(annieamorim@id.uff.br)
7 Deborah Cholodoysky(deborahp@id.uff.br)
8 Kaio Pereira(kaiopereira@id.uff.br)
9 Gabriel Assis(assisgabriel@id.uff.br)
10 Arthur Poustka(arthur_alves@id.uff.br)
11 Paulo Alves(paulosoaresalves@id.uff.br)
12 Andressa Nemirovsky(andressakne@gmail.com)
13 Nathalia Moura( nathaliahmoura@gmail.com)
14 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Chan, W. W.-Y. (2006). A survey on multivariate data visualization. Department of Computer Science and Engineering. Hong Kong University of Science and Technology, 8(6):1–29
2 De Frenne, P., Lenoir, J., Luoto, M., Scheffers, B., et al. (2021). Forest microclimates and climate change: Importance, drivers and future research agenda. Global Change Biology, 27(11):2279–2297.
3 de Souza, C. V. F., da Cunha Luz Barcellos, P., Crissaff, L., Cataldi, M., Miranda, F., and Lage, M. (2022). Visualizing simulation ensembles of extreme weather events. Computers & Graphics, 104:162–172.
4 Diehl, A., Pelorosso, L., Delrieux, C., Saulo, C., Ruiz, J., Gr ̈oller, M. E., and Bruckner, S. (2015). Visual analysis of spatio-temporal data: Applications in weather forecasting. In Computer Graphics Forum, num- ber 3 in 34, pages 381–390
5 Esplugues, F. B., Gramaje, M. d. C. P., and Garc ́ıa-Haro, F. J. (2013). T ́ecnicas de miner ́ıa de datos para el an ́alisis de periodos de sequ ́ıa en espa ̃na. Revista Tiempo y Clima, 5(30).
6 Kumar, P., Chandra, R., Bansal, C., Kalyanaraman, S., Ganu, T., and Grant, M. (2021). Micro-climate predic- tion - multi scale encoder-decoder based deep learning framework. KDD, page 3128–3138
7 Lu, G. Y. and Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & geosciences, 34(9):1044–1055.
8 Mizutori, M. and Guha-Sapir, D. (2020). Human cost of disasters 2000-2019. Technical report, United Nations Office for Disaster Risk Reduction
9 Morais, L. d. and Ferreira, N. C. (2015). Banco de dados pluviom ́etricos integrados por dados do sensor trmm e estac ̧ ̃oes pluviom ́etricas no estado de goi ́as. Anais Eletr., 17.
10 Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., et al. (2022). Tackling climate change with machine learning. ACM Comput. Surv., 55(2).
11 Salas, D., Liang, X., Navarro, M., Liang, Y., and Luna, D. (2020). An open-data open-model framework for hydrological models’ integration, evaluation and application. Environ. Model. Softw., 126:104622.
12 Thorndahl, S. and Willems, P. (2008). Probabilistic modelling of overflow, surcharge and flooding in urban drainage using the first-order reliability method and parameterization of local rain series. Water Research, 42(1):455–466.