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English Information

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Authors
# Name
1 Fabio Victorino(fabiovict95@gmail.com)
2 Annie Amorim(annieamorim@id.uff.br)
3 Kaio Pereira(kaiopereira@id.uff.br)
4 Gabriel Assis(assisgabriel@id.uff.br)
5 Arthur Poustka(arthur_alves@id.uff.br)
6 Felipe Oliveira(felipe@id.uff.br)
7 Yuri Frota(yuri@ic.uff.br)
8 Andressa Nemirovsky(andressakne@gmail.com)
9 Nathalia Moura( nathaliahmoura@gmail.com)
10 Aline Paes(alinepaes@ic.uff.br)
11 Marcos Lage(mlage@ic.uff.br)
12 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 De Frenne, P., Lenoir, J., Luoto, M., Scheffers, B., et al. (2021). Fo- rest microclimates and climate change: Importance, drivers and future research agenda. Global Change Biology, 27(11):2279–2297.
2 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.
3 Diehl, A., Pelorosso, L., Delrieux, C., Saulo, C., Ruiz, J., Gröller, M. E., and Bruckner, S. (2015). Visual analysis of spatio-temporal data: Applications in weather forecasting. In Computer Graphics Forum, number 3 in 34, pages 381–390.
4 Harby, A. A. and Zulkernine, F. H. (2022). From data warehouse to lakehouse: A comparative review. In Tsumoto, S., Ohsawa, Y., Chen, L., den Poel, D. V., Hu, X., Motomura, Y., Takagi, T., Wu, L., Xie, Y., Abe, A., and Raghavan, V., editors, IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, December 17-20, 2022, pages 389–395. IEEE.
5 Lage, M., Victorino, F., Moreira, G., Sá, B., Paes, A., Amorim, A., Cholodoysky, D., Pereira, K., Assis, G., Poustka, A., Alves, P., Nemirovsky, A., Moura, N., and de Oliveira, D. (2022). @weathernit: uma plataforma orientada a dados para monitoramento de chuvas e ocorreˆncias de eventos climáticos. In Anais Estendidos do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 209–214, Búzios, RJ. SBC.
6 Lu, G. Y. and Wong, D. W. (2008). An adaptive inverse-distance weighting spatial interpolation technique. Computers & geosciences, 34(9):1044– 1055.
7 Pierce, M. E., Miller, M. A., Brookes, E. H., Wong, M., Liu, Y., Afgan, E., Gesing, S., Dahan, M., Marru, S., and Walker, T. (2018). Towards a science gateway reference architecture. In Atkinson, M. P. and Gesing, S., editors, Proceedings of the 10th International Workshop on Science Gateways, Edinburgh, Scotland, UK, 13-15 June, 2018, volume 2357 of CEUR Workshop Proceedings. CEUR-WS.org.
8 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).
9 Thorndahl, S. and Willems, P. (2008). Probabilistic model- ling 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.