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Abstract:

The use of information measures for model selection in geophysical models with subgrid parameterizations is examined. Although the resolved dynamical equations of atmospheric or oceanic global numerical models are well established, the development and evaluation of parameterizations that represent subgrid-scale effects pose a big challenge. For climate studies, the parameters or parameterizations are usually selected according to a root-mean-square error criterion that measures the differences between the model-state evolution and observations along the trajectory. However, inaccurate initial conditions and systematic model errors contaminate root-mean-square error measures. In this work, information theory quantifiers, in particular Shannon entropy, statistical complexity, and Jensen-Shannon divergence, are evaluated as measures of the model dynamics. An ordinal analysis is conducted using the Bandt-Pompe symbolic data reduction in the signals. The proposed ordinal information measures are examined in the two-scale Lorenz-96 system. By comparing the two-scale Lorenz-96 system signals with a one-scale Lorenz-96 system with deterministic and stochastic parameterizations, the study shows that information measures are able to select the correct model and to distinguish the parameterizations, including the degree of stochasticity that results in the closest model dynamics to the two-scale Lorenz-96 system. © 2017AmericanMeteorological Society.

Registro:

Documento: Artículo
Título:Model selection: Using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations
Autor:Pulido, M.; Rosso, O.A.
Filiación:Department of Physics, Facultad de Ciencias Exactas y Naturales y Agrimensura, Universidad Nacional del Nordeste, Argentina
CONICET, Corrientes, Argentina
Instituto de Física, Universidade Federal de Alagoas, Maceió, Brazil
Instituto Tecnológico de Buenos Aires, Argentina
CONICET, Ciudad Autónoma de Buenos Aires, Argentina
Complex Systems Group, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de los Andes, Las Condes, Santiago, Chile
Palabras clave:Data assimilation; Lyapunov vectors; Optimization; Subgrid-scale processes; Errors; Information theory; Mean square error; Optimization; Parameterization; Stochastic models; Stochastic systems; Systematic errors; Turbulent flow; Data assimilation; Information measures; Jensen-Shannon divergence; Lyapunov vectors; Root mean square errors; Statistical complexity; Sub-grid scale process; Subgrid-scale effects; Climate models; atmospheric modeling; data assimilation; numerical model; optimization; parameterization; vector
Año:2017
Volumen:74
Número:10
Página de inicio:3253
Página de fin:3269
DOI: http://dx.doi.org/10.1175/JAS-D-16-0340.1
Título revista:Journal of the Atmospheric Sciences
Título revista abreviado:J. Atmos. Sci.
ISSN:00224928
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00224928_v74_n10_p3253_Pulido

Referencias:

  • Abarbanel, H.D.I., (1996) Analysis of Observed Chaotic Data, p. 272. , Springer-Verlag
  • Aksoy, A., Parameter estimation (2015) Encyclopedia of Atmospheric Sciences, pp. 181-186. , 2nd ed. G. R. North, J. Pyle, and F. Zhang, Eds., Elsevier
  • Arnold, H.M., Moroz, I.M., Palmer, T.N., Stochastic parametrizations and model uncertainty in the Lorenz '96 system (2013) Philos. Trans. Roy. Soc, A371. , https://doi.org/10.1098/rsta.2011.0479.Crossref, London
  • Bandt, C., Pompe, B., Permutation entropy: A natural complexity measure for time series (2002) Phys. Rev. Lett, 88, p. 174102
  • Bollt, E., Stanford, T., Lai, Y.-C., Zyczkowski, K., Validity of threshold-crossing analysis of symbolic dynamics from chaotic time series (2000) Phys. Rev. Lett, 85, p. 3524
  • Brissaud, J.B., The meanings of entropy (2005) Entropy, 7, pp. 68-96
  • Charbonneau, P., (2002) An introduction to genetic algorithms for numerical optimization, p. 74. , NCAR Tech. Note TN-450+IA
  • Christensen, H.M., Moroz, I.M., Palmer, T.N., Stochastic and perturbed parameter representations of model uncertainty in convection parameterization (2015) J. Atmos. Sci, 72, pp. 2525-2544
  • Crommelin, D., Vanden-Eijnden, E., Subgrid-scale parameterization with conditional Markov chains (2008) J. Atmos. Sci, 65, pp. 2661-2675
  • DelSole, T., Yang, X., State and parameter estimation in stochastic dynamical models (2010) Physica D, 239, pp. 1781-1788
  • Eckmann, J.-P., Ruelle, D., Ergodic theory of chaos and strange attractors (1985) Rev. Mod. Phys, 57, pp. 617-656
  • Gray, R.M., (1990) Entropy and Information Theory, p. 409. , https://doi.org/10.1007/978-1-4419-7970-4, Springer
  • Grosse, I., Bernaola-Galván, P., Carpena, P., Román-Roldán, R., Oliver, J., Stanley, H.E., Analysis of symbolic sequences using the Jensen-Shannon divergence (2002) Phys. Rev, 65E. , https://doi.org/10.1103/PhysRevE.65.041905
  • Kantz, H., Kurths, J., Meyer-Kress, G., (1998) Nonlinear Analysis of Physiological Data, p. 344. , https://doi.org/10.1007/978-3-642-71949-3, Springer
  • Khinchin, I., (1957) Mathematical Foundations of the Information Theory, p. 128. , Dover Publications
  • Klinker, E., Sardeshmukh, P., The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements (1992) J. Atmos. Sci, 49, pp. 608-627
  • Lamberti, P.W., Martín, M.T., Plastino, A., Rosso, O.A., Intensive entropic non-triviality measure (2004) Physica A, 334, pp. 119-131
  • Lang, M., Van Leeuwen, P.J., Browne, P., A systematic method of parameterisation estimation using data assimilatio (2016) Tellus, 68A. , https://doi.org/10.3402/tellusa.v68.29012.Crossref
  • Leung, L.-Y., North, G.R., Information theory and climate prediction (1990) J. Climate, 3, pp. 5-14
  • Lin, J., Divergence measures based on the Shannon entropy (1991) IEEE Trans. Inf. Theory, 37, pp. 145-151
  • López-Ruiz, R., Mancini, H.L., Calbet, X., A statistical measure of complexity (1995) Phys. Lett, 209A, pp. 321-326. , https//doi.org/10.1016/0375-9601(95)00867-5.Crossref
  • Lorenz, E.N., Predictability-A problem partly solved (1996) Proc. Seminar on Predictability, pp. 1-18. , Shinfield Park, Reading, United Kingdom, European Centre for Medium-Range Weather Forecasting
  • Lott, F., Guez, L., Maury, P., A stochastic parameterization of non-orographic gravity waves: Formalism and impact on the equatorial stratosphere (2012) Geophys. Res. Lett, 39, p. L06807
  • Majda, A.J., Gershgorin, B., Improving model fidelity and sensitivity for complex systems through empirical information theory (2011) Proc. Natl. Acad. Sci. USA, 108, pp. 10044-10049
  • Martín, M.T., Plastino, A., Rosso, O.A., Generalized statistical complexity measures: Geometrical and analytical properties (2006) Physica A, 369, pp. 439-462
  • Palmer, T.N., A nonlinear dynamical perspective on model error: A proposal for non-local stochastic-dynamic parameterization in weather and climate prediction models (2001) Quart. J. Roy. Meteor. Soc, 127, pp. 279-304
  • Pesin, Y.B., Characteristic Lyapunov exponents and smooth ergodic theory (1977) Russ. Math. Surv, 32, p. 55
  • Piani, C., Norton, W.A., Stainforth, D.A., Equatorial stratospheric response to variations in deterministic and stochastic gravity wave parameterizations (2004) J. Geophys. Res, 109, pp. 1984-2012
  • Powell, M.J., The NEWUOA software for unconstrained optimization without derivatives (2006) Large-Scale Nonlinear Optimization, 83, pp. 255-297. , G. D. Pillo and M. Roma, Eds., Nonconvex Optimization and Its Applications, Springer
  • Pulido, M., A simple technique to infer the missing gravity wave drag in the middle atmosphere using a general circulation model: Potential vorticity budget (2014) J. Atmos. Sci, 71, pp. 683-696
  • Pulido, M., Polavarapu, S., Shepherd, T.G., Thuburn, J., Estimation of optimal gravity wave parameters for climate models using data assimilation (2012) Quart. J. Roy. Meteor. Soc, 138, pp. 298-309
  • Pulido, M., Scheffler, G., Ruiz, J., Lucini, M., Tandeo, P., Estimation of the functional form of subgrid-scale parameterizations using ensemble-based data assimilation: A simple model experiment (2016) Quart. J. Roy. Meteor. Soc, 142, pp. 2974-2984
  • Rodwell, M.J., Palmer, T.N., Using numerical weather prediction to assess climate models (2007) Quart. J. Roy. Meteor. Soc, 133, pp. 129-146
  • Rosso, O.A., Masoller, C., Detecting and quantifying stochastic and coherence resonances via information-theory complexity measurement (2009) Phys. Rev., 79E
  • Rosso, O.A., Masoller, C., Detecting and quantifying temporal correlations in stochastic resonance via information theory measures (2009) Eur. Phys. J, 69, pp. 37-43. , https//doi.org/10.1140/epjb/e2009-00146-y.Crossref
  • Rosso, O.A., Larrondo, H.A., Martín, M.T., Plastino, A., Fuentes, M.A., Distinguishing noise from chaos (2007) Phys. Rev. Lett, 99, p. 154102
  • Rosso, O.A., Carpi, L.C., Saco, P.M., Gómez Ravetti, M., Larrondo, H., Plastino, A., The Amigó paradigm of forbidden/missing patterns: A detailed analysis (2012) Eur. Phys. J, 85B, pp. 419-430. , https//doi.org/10.1140/epjb/e2012-30307-8.Crossref
  • Rosso, O.A., Carpi, L.C., Saco, P.M., Gómez Ravetti, M., Plastino, A., Larrondo, H., Causality and the entropy-complexity plane: Robustness and missing ordinal patterns (2012) Physica A, 391, pp. 42-55
  • Ruiz, J., Pulido, M., Parameter estimation using ensemble based data assimilation in the presence of model error (2015) Mon. Wea. Rev, 143, pp. 1568-1582
  • Ruiz, J., Pulido, M., Miyoshi, T., Estimating model parameters with ensemble-based data assimilation (2013) A review. J. Meteor. Soc. Japan, 91, pp. 79-99. , https://doi.org/10.2151/jmsj.2013-201.Crossref
  • Ruiz, J., Pulido, M., Miyoshi, T., Estimating parameters with ensemble-based data assimilation: Parameter covariance treatment (2013) J. Meteor. Soc. Japan, 91, pp. 453-469
  • Schuster, H.G., Just, W., (2006) Deterministic Chaos: An Introduction, p. 320. , John Wiley and Sons
  • Serinaldi, F., Zunino, L., Rosso, O.A., Complexity-entropy analysis of daily stream flow time series in the continental United States (2014) Stochastic Environ. Res. Risk Assess, 28, pp. 1685-1708
  • Shannon, C.E., A mathematical theory of communication (1948) Bell Syst. Tech. J, 27, pp. 379-423
  • Shannon, C.E., Weaver, W., (1949) The Mathematical Theory of Communication, p. 125. , University of Illinois Press
  • Shutts, G., A kinetic energy backscatter algorithm for use in ensemble prediction systems (2005) Quart. J. Roy. Meteor. Soc, 131, pp. 3079-3102
  • Sippel, S., Lange, H., Mahecha, M.D., Hauhs, M., Bodesheim, P., Kaminski, T., Gans, F., Rosso, O.A., Diagnosing the dynamics of observed and simulated ecosystem gross primary productivity with time causal information theory quantifiers (2016) PLoS One, 11
  • Stainforth, D.A., Uncertainty in predictions of the climate response to rising levels of greenhouse gases (2005) Nature, 433, pp. 403-406
  • Tirabassi, G., Massoller, C., Unravelling the community structure of the climate system by using lags and symbolic time-series analysis (2016) Sci. Rep, 6, p. 29804
  • Wilks, D.S., Effects of stochastic parameterizations in the Lorenz '96 system (2005) Quart. J. Roy. Meteor. Soc, 131, pp. 389-407
  • Zanin, M., Zunino, L., Rosso, O.A., Papo, D., Permutation entropy and its main biomedical and econophysics applications: A review (2012) Entropy, 14, pp. 1553-1577

Citas:

---------- APA ----------
Pulido, M. & Rosso, O.A. (2017) . Model selection: Using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations. Journal of the Atmospheric Sciences, 74(10), 3253-3269.
http://dx.doi.org/10.1175/JAS-D-16-0340.1
---------- CHICAGO ----------
Pulido, M., Rosso, O.A. "Model selection: Using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations" . Journal of the Atmospheric Sciences 74, no. 10 (2017) : 3253-3269.
http://dx.doi.org/10.1175/JAS-D-16-0340.1
---------- MLA ----------
Pulido, M., Rosso, O.A. "Model selection: Using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations" . Journal of the Atmospheric Sciences, vol. 74, no. 10, 2017, pp. 3253-3269.
http://dx.doi.org/10.1175/JAS-D-16-0340.1
---------- VANCOUVER ----------
Pulido, M., Rosso, O.A. Model selection: Using information measures from ordinal symbolic analysis to select model subgrid-scale parameterizations. J. Atmos. Sci. 2017;74(10):3253-3269.
http://dx.doi.org/10.1175/JAS-D-16-0340.1