Towards a Definition for Extreme Weather Events in Rio de Janeiro City
Abstract
Extreme weather events is a new area of research that has recently attracted the attention of researchers from different disciplines. In this paper, we investigate the phenomena from a data-driven forecast standpoint for severe rainfall occurring in the city of Rio de Janeiro. We aim at a formal definition for the phenomena that can clarify its concept and help drive the research. Our initial result is a characterization of the problem inspired by a reformulation of a numerical model based forecast framework.
Keywords:
Machine learning, rainfall forecast, extreme events
References
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T. Buch-larsen, J. P. Nielsen, M. Guillén, and C. Bolancé. Kernel density estimation for heavy-tailed distributions using the champernowne transformation. Statistics, 39(6): 503–516, 2005. doi: 10.1080/02331880500439782.
F. daSilva. Projeto pesquisa operacional, 2019. Internal Report, in PT.
C. Dereczynski, R. Calado, and A. Barros. Extreme rainfall in the city of rio de janeiro: History from the 19th century. Anuário do Instituto de Geociências - UFRJ, 40:17–30, 09 2017. doi: 10.11137/2017_2_17_30.
R. N. d’Orsi, N. M. Paes, M. A. Magalhães, R. da Silva Coelho, L. R. da Silva Junior, and L. R. S. Valente. Os 50 maiores acidentes geológico-geotécnicos na cidade do rio de janeiro entre 1966 e 2016. [link]., 2017. Accessed: 2022-06-24.
M. Farazmand and T. Sapsis. Extreme events: Mechanisms and prediction. Applied Mechanics reviews, 71(5), Sept. 2019. doi: https://doi.org/10.1115/1.4042065.
B. Gates. How to avoid a Climate Disaster: The Solutions We Have and the Breakthroughs We Need. Random House Large Print Publishing, 2021.
L. Haans and A. Ferreira. Extreme Value Theory An Introduction. Springer, 2006. ISBN 978-0-387-34471-3.
V. Lucarini, D. Faranda, A. de Freitas, J. de Freitas, J. Holland, T. Kuna, M. Nicol, M. Todd, and S. Vaienti. Extremes and Recurrence in Dynamical Systems. Wiley, 2016. ISBN 978-1-118-63219-2.
A. Luiz-Silva, W.and Oscar-Júnior. Climate extremes related with rainfall in the state of rio de janeiro, brazil: a review of climatological characteristics and recorded trends. Nat Hazards, (10), 2022. doi: https://doi.org/10.1007/s11069-022-05409-5.
M. Mudelsee. Extreme Value Time Series. Springer, 2010.
F. Porto, M. Ferro, E. Ogasawara, T. Moeda, C. D. Tenorio de Barros, A. Chaves Silva, R. Zorrilla, R. Silva Pereira, R. Nascimento Castro, J. V. Silva, R. Salles, A. J. Fonseca, J. Hermsdorff, M. Magalhães, V. Sá, A. A. Simões, C. Cardoso, and E. Bezerra. Machine learning approaches to extreme weather events forecast in urban areas: Challenges and initial results. Supercomputing Frontiers and Innovations, 9(1):49–73, May 2022. doi: 10.14529/jsfi220104.
H. Touchette. A basic introduction to large deviations: Theory, applications, simulations. 2011. doi: 10.48550/ARXIV.1106.4146.
Y. Wang, E. Coning, A. Harou, W. Jacobs, P. Joe, L. Nikitina, R. Roberts, J. Wang, J.Wilson, A. Atencia, B. Bica, B. Brown, S. Goodmann, A. Kann, P.-w. Li, I. Monterio, F. Schmid, A. Seed, and J. Sun. Guidelines for Nowcasting Techniques. 11 2017. ISBN 978-92-63-11198-2.
G. S.Watson. Extreme values in samples from m-dependent stationary stochastic processes. The Annals of Mathematical Statistics, 25(4):798–800, 1954.
Y. Xiang, J. Ma, and X. Wu. A precipitation nowcasting mechanism for real-world data based on machine learning. Mathematical Problems in Engineering, 2020:1–11, 11 2020. doi: 10.1155/2020/8408931.
Published
2022-09-19
How to Cite
FERRO, Mariza; BEZERRA, Eduardo; OGASAWARA, Eduardo; MORAES, Nilton; PORTO, Fabio.
Towards a Definition for Extreme Weather Events in Rio de Janeiro City. In: WORKSHOP ON DATA-DRIVEN EXTREME EVENTS ANALYTICS - BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 181-186.
DOI: https://doi.org/10.5753/sbbd_estendido.2022.21862.
