Artículo

Metref, S.; Hannart, A.; Ruiz, J.; Bocquet, M.; Carrassi, A.; Ghil, M."Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem" (2019) Quarterly Journal of the Royal Meteorological Society
Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

In recent years, there has been increased interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. In this setting, previous studies introduced the contextual formulation of model evidence, or contextual model evidence (CME), and showed that CME can be efficiently computed using a hierarchy of ensemble-based DA procedures. Although these studies analysed the DA methods most commonly used for operational atmospheric and oceanic prediction worldwide, they did not study these methods in conjunction with localization to a specific domain. Yet, any application of ensemble DA methods to realistic, very high-dimensional geophysical models requires the implementation of some form of localization. The present study extends CME estimation to ensemble DA methods with domain localization. Domain-localized CME (DL-CME) developed in this article is tested for model selection with two models: (a) the Lorenz 40-variable midlatitude atmospheric dynamics model (Lorenz-95); and (b) the simplified global atmospheric SPEEDY model. CME is compared to the root-mean-square error (RMSE) as a metric for model selection. The experiments show that CME systematically outperforms RMSE in model selection skill, and that this skill improvement is further enhanced by applying localization to the CME estimate using DL-CME. The potential use and range of applications of CME and DL-CME as a model selection metric are also discussed. © 2019 Royal Meteorological Society

Registro:

Documento: Artículo
Título:Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem
Autor:Metref, S.; Hannart, A.; Ruiz, J.; Bocquet, M.; Carrassi, A.; Ghil, M.
Filiación:IFAECI, CNRS-CONICET-UBA, Buenos Aires, Argentina
CIMA-CONICET, University of Buenos Aires, Buenos Aires, Argentina
CEREA, Joint Laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est, Champs-sur-Marne, France
Nansen Environmental and Remote Sensing Center, Bergen, Norway
Geosciences Department and Laboratoire de Météorologie Dynamique (CNRS and IPSL), École Normale Supérieure and PSL Research University, Paris, France
Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA, Afghanistan
Palabras clave:contextual model evidence; detection and attribution; ensemble Kalman filter; localization; parameter estimation; Earth atmosphere; Meteorology; Parameter estimation; Atmospheric dynamics; Contextual modeling; Detection and attributions; Ensemble based data assimilation; Ensemble Kalman Filter; localization; Model selection problem; Root mean square errors; Mean square error
Año:2019
DOI: http://dx.doi.org/10.1002/qj.3513
Handle:http://hdl.handle.net/20.500.12110/paper_00359009_v_n_p_Metref
Título revista:Quarterly Journal of the Royal Meteorological Society
Título revista abreviado:Q. J. R. Meteorol. Soc.
ISSN:00359009
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00359009_v_n_p_Metref

Referencias:

  • Akaike, H., A new look at the statistical model identification (1974) IEEE Transactions on Automatic Control, 19, pp. 716-723
  • Ando, T., (2010) Bayesian Model Selection and Statistical Modeling, , Boca Raton, FL, CRC Press
  • Asch, M., Bocquet, M., Nodet, M., (2016) Data Assimilation: Methods, Algorithms, and Applications, , Philadelphia, PA, SIAM
  • Balgovind, R., Dalcher, A., Ghil, M., Kalnay, E., A stochastic-dynamic model for the spatial structure of forecast error statistics (1983) Monthly Weather Review, 111, pp. 701-722
  • Baum, L.E., Petrie, T., Soules, G., Weiss, N., A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains (1970) The Annals of Mathematical Statistics, 41, pp. 164-171
  • Bennett, A.F., (1992) Inverse Methods in Physical Oceanography, , Cambridge, Cambridge University Press
  • Bishop, C.H., Etherton, B.J., Majumdar, S.J., Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects (2001) Monthly Weather Review, 129, pp. 420-436
  • Bocquet, M., Localization and the iterative ensemble Kalman smoother (2016) Quarterly Journal of the Royal Meteorological Society, 142, pp. 1075-1089
  • Bocquet, M., Carrassi, A., Four-dimensional ensemble variational data assimilation and the unstable subspace (2017) Tellus A, 69, p. 1304504
  • Bourke, W., A multilevel spectral model. I. Formulation and hemispheric integrations (1974) Monthly Weather Review, 102, pp. 687-701
  • Bracco, A., Kucharski, F., Kallumal, R., Molteni, F., Internal variability, external forcing and climate trends in multi-decadal AGCM ensembles (2004) Climate Dynamics, 23, pp. 659-678
  • Buehner, M., Houtekamer, P.L., Charette, C., Mitchell, H.B., Intercomparison of variational data assimilation and the ensemble Kalman filter for global deterministic NWP. Part I: description and single-observation experiments (2010) Monthly Weather Review, 138, pp. 1550-1566
  • Burnham, K.P., Anderson, D.R., (2002) Model Selection and Inference: A Practical Information-Theoretic Approach, , 2nd ed., New York, NY, Springer
  • Carrassi, A., Vannitsem, S., Deterministic treatment of model error in geophysical data assimilation (2016) Mathematical Paradigms of Climate Science, pp. 175-213. , Cham, Springer
  • Carrassi, A., Bocquet, M., Hannart, A., Ghil, M., Estimating model evidence using data assimilation (2017) Quarterly Journal of the Royal Meteorological Society, 143, pp. 866-880
  • Carrassi, A., Bocquet, M., Bertino, L., Evensen, G., Data assimilation in the geosciences: an overview of methods, issues, and perspectives (2018) WIREs Climate Change, 9
  • Carson, J., Crucifix, M., Preston, S., Wilkinson, R.D., Bayesian model selection for the glacial–interglacial cycle (2018) Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, pp. 25-54
  • Cressman, G.P., An operational objective analysis system (1959) Monthly Weather Review, 87, pp. 367-374
  • Daley, R., (1991) Atmospheric Data Analysis, , Cambridge, Cambridge University Press
  • Dee, D.P., On-line estimation of error covariance parameters for atmospheric data assimilation (1995) Monthly Weather Review, 123, pp. 1128-1145
  • Del Moral, P., (2004) Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications, , New York, Springer
  • Donner, L.J., Seman, C.J., Hemler, R.S., Fan, S., A cumulus parameterization including mass fluxes, convective vertical velocities, and mesoscale effects: thermodynamic and hydrological aspects in a general circulation model (2001) Journal of Climate, 14, pp. 3444-3463
  • Elsheikh, A., Hoteit, I., Wheeler, M., Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates (2014) Computer Methods in Applied Mechanics and Engineering, 269, pp. 515-537
  • Elsheikh, A., Wheeler, M., Hoteit, I., Hybrid nested sampling algorithm for Bayesian model selection applied to inverse subsurface flow problems (2014) Journal of Computational Physics, 258, pp. 319-337
  • Evensen, G., (2009) Data Assimilation: The Ensemble Kalman Filter, , 2nd ed., Berlin, Springer-Verlag
  • Gaspari, G., Cohn, S.E., Construction of correlation functions in two and three dimensions (1999) Quarterly Journal of the Royal Meteorological Society, 125, pp. 723-757
  • Ghil, M., Advances in sequential estimation for atmospheric and oceanic flows (1997) Journal of the Meteorological Society of Japan, 75, pp. 289-304
  • Ghil, M., Malanotte-Rizzoli, P., Data assimilation in meteorology and oceanography (1991) Advances in Geophysics, 33, pp. 141-266
  • Ghil, M., Halem, M., Atlas, R., Time-continuous assimilation of remote-sounding data and its effect on weather forecasting (1979) Monthly Weather Review, 107, pp. 140-171
  • Gini, C., Measurement of inequality of incomes (1921) The Economic Journal, 31, pp. 124-126
  • Greybush, S.J., Kalnay, E., Miyoshi, T., Ide, K., Hunt, B.R., Balance and ensemble Kalman filter localization techniques (2011) Monthly Weather Review, 139, pp. 511-522
  • Haarsma, R.J., Campos, E.J.D., Molteni, F., Atmospheric response to South Atlantic SST dipole (2003) Geophysical Research Letters, 30, p. 1864
  • Hamill, T.M., Whitaker, J.S., Snyder, C., Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter (2001) Monthly Weather Review, 129, pp. 2776-2790
  • Hannart, A., Carrassi, A., Bocquet, M., Ghil, M., Naveau, P., Pulido, M., Ruiz, J., Tandeo, P., DADA: data assimilation for the detection and attribution of weather and climate-related events (2016) Climatic Change, 136, pp. 155-174
  • Hannart, A., Pearl, J., Otto, F.E.L., Naveau, P., Ghil, M., Causal counterfactual theory for the attribution of weather and climate-related events (2016) Bulletin of the American Meteorological Society, 97, pp. 99-110
  • Harlim, J., Model error in data assimilation (2017) Nonlinear and Stochastic Climate Dynamics, pp. 276-317. , In C.L.E. Franzke and T.J. O'Kane (Eds.),, Cambridge, Cambridge University Press
  • Houtekamer, P.L., Mitchell, H.L., A sequential ensemble Kalman filter for atmospheric data assimilation (2001) Monthly Weather Review, 129, pp. 123-137
  • Hunt, B., Kostelich, E.J., Szunyogh, I., Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter (2007) Physica D, 230, pp. 112-126
  • Ide, K., Courtier, P., Ghil, M., Lorenc, A., Unified notation for data assimilation: operational, sequential and variational (1997) Journal of the Meteorological Society of Japan, 75, pp. 181-189
  • Kalnay, E., (2003) Atmospheric Modeling, Data Assimilation and Predictability, , Cambridge, Cambridge University Press
  • Kucharski, F., Molteni, F., On non-linearities in a forced North Atlantic Oscillation (2003) Climate Dynamics, 21, pp. 677-687
  • Kucharski, F., Molteni, F., Bracco, A., Decadal interactions between the western tropical Pacific and the North Atlantic Oscillation (2006) Climate Dynamics, 26, pp. 79-91
  • Lermusiaux, P.F.J., Uncertainty estimation and prediction for interdisciplinary ocean dynamics (2006) Journal of Computational Physics, 217, pp. 176-199
  • Lorenz, E.N., Deterministic nonperiodic flow (1963) Journal of the Atmospheric Sciences, 20, pp. 130-141
  • Lorenz, E.N., (1995) Predictability: a problem partly solved. In: Seminar on Predictability, 4–8 September 1995, Shinfield Park, UK, Reading: ECMWF.
  • Lorenz, E.N., Emanuel, K.A., Optimal sites for supplementary weather observations: simulation with a small model (1998) Journal of the Atmospheric Sciences, 55, pp. 399-414
  • Metz, C.E., Basic principles of ROC analysis (1978) Seminars in Nuclear Medicine, 8, pp. 283-298
  • Molteni, F., Atmospheric simulations using a GCM with simplified physical parameterizations. I: model climatology and variability in multi-decadal experiments (2003) Climate Dynamics, 20, pp. 175-191
  • Miyoshi, T., (2005) Ensemble Kalman filter experiments with a primitive-equation global model. PhD Dissertation, University of Maryland.
  • Miyoshi, T., Yamane, S., Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution (2007) Monthly Weather Review, 135, pp. 3841-3861
  • Miyoshi, T., Yamane, S., Enomoto, T., Localizing the error covariance by physical distances within a local ensemble transform Kalman filter (LETKF) (2007) Scientific Online Letters on the Atmosphere, 3, pp. 89-92
  • Navon, I.M., Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography (1997) Dynamics of Atmospheres and Oceans, 27, pp. 55-79
  • Neelin, J.D., Bracco, A., Luo, H., McWilliams, J.C., Meyerson, J.E., Considerations for parameter optimization and sensitivity in climate models (2010) Proceedings of the National Academy of Sciences of the United States of America, 107, pp. 21349-21354
  • Otsuka, S., Miyoshi, T., A Bayesian optimization approach to multimodel ensemble Kalman filter with a low-order model (2015) Monthly Weather Review, 143, pp. 2001-2012
  • Ott, E., Hunt, B.R., Szunyogh, I., Corazza, M., Kalnay, E., Patil, D.J., Yorke, J.A., Kostelich, E.J., (2002) Exploiting local low dimensionality of the atmospheric dynamics for efficient ensemble Kalman filtering
  • Ott, E., Hunt, B.R., Szunyogh, I., Zimin, A.V., Kostelich, E.J., Corazza, M., Kalnay, E., Yorke, A., A local ensemble Kalman filter for atmospheric data assimilation (2004) Tellus A, 56, pp. 415-428
  • Raanes, P.N., Carrassi, A., Bertino, L., Extending the square root method to account for additive forecast noise in ensemble methods (2015) Monthly Weather Review, 143, pp. 3857-3873
  • Reich, S., Cotter, C., (2015) Probabilistic Forecasting and Bayesian Data Assimilation, , Cambridge, Cambridge University Press
  • Saha, S., Moorthi, S., Pan, H.L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Goldberg, M., The NCEP climate forecast system reanalysis (2010) Bulletin of the American Meteorological Society, 91, pp. 1010-1057
  • Saha, S., Shrinivas, M., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Becker, E., The NCEP climate forecast system version 2 (2014) Journal of Climate, 27, pp. 2185-2208
  • Sakov, P., Bertino, L., Relation between two common localisation methods for the EnKF (2011) Computers & Geosciences, 15, pp. 225-237
  • Sakov, P., Counillon, F., Bertino, L., Lisaeter, K.A., Oke, P.R., Korablev, A., TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic (2012) Ocean Science, 8, pp. 633-656
  • Särkkä, S., (2013) Bayesian Filtering and Smoothing, 3. , Cambridge, Cambridge University Press
  • Schwarz, G., Estimating the dimension of a model (1978) The Annals of Statistics, 6, pp. 461-464
  • Szunyogh, I., Kostelich, E.J., Gyarmati, G., Kalnay, E., Hunt, B.R., Ott, E., Satterfield, E., Yorke, J.A., A local ensemble transform Kalman filter data assimilation system for the NCEP global model (2008) Tellus A, 60, pp. 113-130
  • Tiedke, M., A comprehensive mass flux scheme for cumulus parametrization in large-scale models (1993) Monthly Weather Review, 117, pp. 1779-1800
  • Whitaker, J.S., Hamill, T.M., Wei, X., Song, Y., Toth, Z., Ensemble data assimilation with the NCEP global forecast system (2008) Monthly Weather Review, 136, pp. 463-482
  • Winiarek, V., Vira, J., Bocquet, M., Sofiev, M., Saunier, O., Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release (2011) Atmospheric Environment, 45, pp. 2944-2955

Citas:

---------- APA ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A. & Ghil, M. (2019) . Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem. Quarterly Journal of the Royal Meteorological Society.
http://dx.doi.org/10.1002/qj.3513
---------- CHICAGO ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A., Ghil, M. "Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem" . Quarterly Journal of the Royal Meteorological Society (2019).
http://dx.doi.org/10.1002/qj.3513
---------- MLA ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A., Ghil, M. "Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem" . Quarterly Journal of the Royal Meteorological Society, 2019.
http://dx.doi.org/10.1002/qj.3513
---------- VANCOUVER ----------
Metref, S., Hannart, A., Ruiz, J., Bocquet, M., Carrassi, A., Ghil, M. Estimating model evidence using ensemble-based data assimilation with localization – The model selection problem. Q. J. R. Meteorol. Soc. 2019.
http://dx.doi.org/10.1002/qj.3513