References

  1. IPCC, Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, P.M. Midgley, Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013, p. 1535.
  2. J.J. González-Alemán, S. Pascale, J. Gutierrez-Fernandez, H. Murakami, M.A. Gaertner, G.A. Vecchi, Potential increase in hazard from Mediterranean hurricane activity with global warming, Geophys. Res. Lett., 46 (2019) 1754–1764.
  3. R.A. Pielkejr, C. Landsea, M. Mayfield, J. Layer, R. Pasch, Hurricanes and global warming, Bull. Am. Meteorol. Soc., 86 (2005) 1571–1575.
  4. K. Emanuel, R. Sundararajan, J. Williams, Hurricanes and global warming: results from downscaling IPCC AR4 simulations, Bull. Am. Meteorol. Soc., 89 (2008) 347–367.
  5. Y.-K. Lim, S.D. Schubert, R. Kovach, A.M. Molod, S. Pawson, The roles of climate change and climate variability in the 2017 Atlantic Hurricane season, Sci. Rep., 8 (2018) 16172.
  6. R. Romero, K. Emanuel, Medicane risk in a changing climate, J. Geophys. Res. Atmos., 118 (2013) 5992–6001.
  7. M.A. Gaertner, J.J. González-Alemán, R. Romera, M. Domínguez, V. Gil, E. Sánchez, C. Gallardo, M.M. Miglietta, K.J.E. Walsh, D.V. Sein, S. Somot, A. Dell’Aquila, C. Teichmann, B. Ahrens, E. Buonomo, A. Colette, S. Bastin, E. van Meijgaard, G. Nikulin, Simulation of medicanes over the Mediterranean Sea in a regional climate model ensemble: impact of ocean– atmosphere coupling and increased resolution, Clim. Dyn., 51 (2018) 1041–1057.
  8. ACTION MODULERS, Mohid Studio. Available at: http://actionmodulers.pt/products/mstudio/products-mohidstudio 2015.shtml (ref. March 2, 2019).
  9. MOHID Water Modelling System. Available at: www.mohid. com (ref. March 02, 2019).
  10. G.A. Franz, P. Leitão, A. Santos, M. Juliano, R. Neves, From regional to local scale modelling on the south-eastern Brazilian shelf: case study of Paranaguá estuarine system, Braz. J. Oceanogr., 64 (2016) 277–294.
  11. P.M. Paiva, J. Lugon Jr., A.N. Barreto, J.F. Silva, A.J. Silva Neto, Comparing 3D and 2D computational modeling of an oil well blowout using MOHID platform – a case study in the Campos Basin, Sci. Total Environ., 595 (2017) 633–641.
  12. J. Lugon Jr., F.A. Kalas, P.P.G.W. Rodrigues, J.L. Jeveaux, H. Gallo Neto, M.M. Juliano, A.J. Silva Neto, Lagrangian trajectory simulation of floating objects in the state of São Paulo coastal region, Defect Diffus. Forum, 396 (2019) 42–49.
  13. GEBCO, Gridded Bathymetry Data, General Bathymetric Chart of Oceans. Available at: https://www.gebco.net/data\_and\_ products/gridded\_bathymetry\_data (ref. November 20, 2017).
  14. GFS, GFS Analysis, Global Forecast System. Available at: https:// www.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs (ref. November 20, 2017).
  15. L. Carrére, F. Lyard, M. Cancet, A. Guillot, L. Roblout, FES2012: A New Global Tidal Model Taking Advantage of Nearly 20 Years of Altimetry, In: Proceedings of the Meeting 20 Years of Altimetry, Venice, 2012.
  16. COPERNICUS, Marine Environment Monitoring Service. Available at: http://marine.copernicus.eu/services-portfolio/accessto-products/?option=com\_csw\&task=results (ref. November 20, 2017).
  17. G. Mellor, T. Yamada, Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys. Space Phys., 20 (1982) 851–875.
  18. Permanent Service for Mean Sea Level (Isabella de Sagua Station). Available at: https://www.psmsl.org/data/obtaining/ stations/411.php (ref. March 2, 2019).
  19. Sea Level Station Monitoring Facility (Key West Station, USA). Available at: http://www.ioc-sealevelmonitoring.org/station. php?code=kwfl (ref. March 2, 2019).
  20. Permanent Service for Mean Sea Level (Kalamai Station, Greece). Available at: https://www.psmsl.org/data/obtaining/ stations/411.php (ref. March 2, 2019).
  21. Sea Level Station Monitoring Facility (Katacolo Station, Greece). Available at: http://www.ioc-sealevelmonitoring.org/station. php?code=kata (ref. March 2, 2019).
  22. F.D. Moura Neto, A.J. Silva Neto, An Introduction to Inverse Problems with Applications, Springer-Verlag Berlin, Heidelberg, ISBN 978–3-642–32556–4, 2013, p. 246.
  23. J. Lugon Jr., A.J. Silva Neto, P.P.G.W. Rodrigues, Assessment of dispersion mechanisms in rivers by means of an inverse problem approach, Inverse Prob. Sci. Eng., 16 (2008) 967–979.
  24. C. Oliveira, J. Lugon Jr., D.C. Knupp, A.J. Silva Neto, A. Prieto-Moreno, O. Llanes-Santiago, Estimation of kinetic parameters in a chromatographic separation model via Bayesian inference, Rev. Int. Métodos Numér. Cálc. Diseño Ing., 34 (2018) 1–13.
  25. J. Lugon Jr, A.J. Silva Neto, Solution of porous media inverse drying problems using a combination of stochastic and deterministic methods, J. Braz. Soc. Mech. Sci. Eng., 33 (2011) 400–407.