Data Assimilation on CoLab Environment: Methods and Application on Two Dynamical Systems
DOI:
https://doi.org/10.5540/03.2023.010.01.0037Palabras clave:
Data assimilation methods, Lorenz system, Shallow water 2D, Octave package, Google Colab platformResumen
Data assimilation (DA) is an essential process to identify the best initial conditional by combining data from an observation system with a previous prediction from a numerical simulation of a given dynamical system. This paper describes the effort to develop a framework for testing different methods applied to two dynamical systems. The framework was implemented using the Google CoLab platform, and Octave free mathematical software. The dynamic systems used for testing is the Lorenz system under the chaotic regime, and 2D shallow water — for ocean circulation modeling.
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P. Bauer, A. Thorpe, and G. Brunet. “The quiet revolution of numerical weather prediction”. Nature, 4.525 (2015), pp. 47–55.
A. F. Bennett. Inverse Modeling of the Ocean and Atmosphere. Cambridge University Press, 2002.
H. F. Campos Velho et al. “Data assimilation by neural network for ocean circulation: parallel implementation”. Supercomputing Frontiers and Innovations, 9.1 (2022), pp. 74–86. url: https://superfri.org/index.php/superfri/article/view/419.
J. Charney, R. Fjørtoft, and J. von Neumann. “Numerical integration of the barotropic vorticity equation”. Tellus 2.4 (1950), pp. 237–254.
R. S. C. Cintra, and H. F. Campos Velho. “Data assimilation by artificial neural networks for an atmospheric general circulation model”. Advanced Applications for Artificial Neural Network. Ed. by A. El-Shahat. InTech, 2008. Chapter 14, pp. 265–285, doi: 10.5772/intechopen.70791. (url: https://www.intechopen.com/chapters/57304)
R. Daley. Atmospheric Data Analysis Atmospheric Data Analysis. Cambridge University Press, 1993.
H. Furtado, H. F. Campos Velho, and E. E. N. Macau. “Assimilação de dados com redes neurais artificiais em equações diferenciais”. Conferência Brasileira de Dinâmica, Controle e Aplicações. 2011. doi: 10.13140/2.1.5055.1687.
GNU-Octave. Octave programming language. On-line. Access 30/March/2023, https://octave.org/.
M. Goodliff, J. Amezcua, and P. J. Van Leeuwen. “Comparing hybrid data assimilation methods on the Lorenz 1963 model with increasing non-linearity”. Tellus A: Dynamic Meteorology and Oceanography 67.1 (2015), p. 26928.
Google-Colab. Free cloud service by Jupyter notebooks. On-line. Access 30/March/2023. https://colab.research.google.com/
S. Haykin. Neural Networks: A Comprehensive Foundation. Prentice Hall Inc., 1994.
E. Kalnay. Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, 2002.
LNCC-MCTI. [MC-A01] Assimilação de Dados por Redes Neurais Artificiais. On-line. Access 30/March/2023, colorblue http://veraolncc.kinghost.net/MinicursosAvulsos.php.
E. N. Lorenz. “Deterministic nonperiodic flow”. Journal of the Atmospheric Sciences 20.2 (1963), pp. 130–141.
E. F. P. Luz, J. C. Becceneri, and H. F. Campos Velho. “A new multi-particle collision algorithm for optimization in a high performance environment”. Journal of Computational Interdisciplinary Sciences 1.1 (2008), pp. 3–10. doi: 110. 6062/jcis.2008.01.01.0002. (ulr; https://www.epacis.net/jcis/PDF JCIS/JCIS11-art.01.pdf)
P. Lynch. The Emergence of Numerical Weather Prediction: Richardson’s Dream. Cambridge University Press, 2006.
L. F. Richardson. Weather Prediction by Numerical Processes. Cambridge University Press, 1992.