Stochastic parameterization identification using ensemble Kalman filtering combined with maximum likelihood methods
References
- Anderson
, J.. An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 2884– 2903. - Bellsky
, T., Berwald, J.and Mitchell, L.. Nonglobal parameter estimation using local ensemble Kalman filtering. Mon. Wea. Rev., 2150– 2164. - Bishop
, C.. Pattern Recognition and Machine Learning. - Briers
, M., Doucet, A.and Maskell, S.. Smoothing algorithms for state-spacemodels. Ann. Inst. Stat. Math., 61– 89. - Cappé
, O., Moulines, E.and Rydén, T.. Inference in Hidden Markov Models. Springer, New York, NY. - Carrassi
, A., Bocquet, M., Hannart, A.and Ghil, M.. Estimating model evidence using data assimilation. Q. J. R. Meteorol. Soc., 866– 880. - Carrassi
, A.and Vannitsem, S.. State and parameter estimation with the extended Kalman filter: an alternative formulation of the model error dynamics. Q. J. R. Meteorol. Soc., 435– 451. - Christensen
, H., Moroz, I. M.and Palmer, T. N.. Stochastic and perturbed parameter representations of model uncertainty in convection parameterization. J. Atmos. Sci., 2525– 2544. - Cosme
, E., Verron, J., Brasseur, P., Blum, J.and Auroux, D.. Smoothing problems in a Bayesian framework and their linear gaussian solutions. Mon. Weather Rev., 683– 695. - Delsole
, T.and Yang, X.. State and parameter estimation in stochastic dynamical models. Physica D, 1781– 1788. - Dempster
, A., Laird, N.and Rubin, D.. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B, 1– 38. - Dreano
, D., Tandeo, P., Pulido, M., Ait-El-Fquih, B., Chonavel, T.and co-authors.. Estimation of error covariances in nonlinear state-space models using the expectation maximization algorithm. Q. J. R. Meteorol. Soc., 1877– 1885. - Evensen
, G.. Sequential data assimilation with a nonlinear quasi-geostrophic model using monte carlo methods to forecast error statistics. J. Geophys. Res., 10143– 10162. - Evensen
, G.. The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn., 343– 367. - Ghahramani
, Z.and Roweis, S.. Learning nonlinear dynamical systems using an EM algorithm. In: Advances in Neural Information Processing Systems, MIT Press, pp. 431– 437. - Hamilton
, F., Berry, T., Peixoto, N.and Sauer, T.. Real-time tracking of neuronal network structure using data assimilation. Phys. Rev. E, 052715. - Hamilton
, F., Berry, T.and Sauer, T.. Ensemble Kalman filtering without a model. Phys. Rev. X, 011021. - Hannart
, A., Carrassi, A., Bocquet, M., Ghil, M., Naveau, P.and co-authors.. Dada: data assimilation for the detection and attribution of weather and climate-related events. Clim. Change, 155– 174. - Hansen
, J.and Penland, C.. Efficient approximate techniques for integrating stochastic differential equations. Mon. Wea. Rev., 3006– 3014. - Hunt
, B., Kostelich, E. J.and Szunyogh, I.. Efficient data assimilation for spatio-temporal chaos: a local ensemble transform Kalman filter. Physica D, 437– 471. - Jazwinski
, A. H.. Stochastic and Filtering Theory. Mathematics in Sciences and Engineering Series , Vol. 64. Academic Press, London and New York, p. 376. - Kalnay
, E.. Atmospheric Modeling, Data Assimilation, and Predictability, Cambridge. - Katsoulakis
, M., Majda, A.and Vlachos, D.. Coarse-grained stochastic processes for microscopic lattice systems. Proc. Nat. Acad. Sci., 782– 787. - Kondrashov
, D., Ghil, M.and Shprits, Y.. Lognormal Kalman filter for assimilating phase space density data in the radiation belts. Space Weather, 11. - Lguensat
, R., Tandeo, P., Fablet, R., Pulido, M.and Ailliot, P.. The analog ensemble-based data assimilation. Mon. Wea. Rev., 4093– 4107. - Lorenz
, E.( 1996). Predictability–A Problem Partly Solved. Reading: ECMWF. (pp. 1– 18) - Lott
, F., Guez, L.and Maury, P.. A stochastic parameterization of nonorographic gravity waves: formalism and impact on the equatorial stratosphere. Geophys. Res. Lett., L06807. - Majda
, A.and Gershgorin, B.. Improving model fidelity and sensitivity for complex systems through empirical information theory. Proc. Nat. Acad. Sci., 10044– 10049. - Mason
, P.and Thomson, D.. Stochastic backscatter in large-eddy simulations of boundary layers. J. Fluid Mech., 51– 78. - Neal
, R.and Hinton, G.. A View of the EM Algorithm that Justifies Incremental, Sparse and other Variants. Springer, Dordrecht. - Nicolis
, N.. Dynamics of model error: the role of unresolved scales revisited. J. Atmos. Sci., 1740– 1753. - Palmer
, T.. A nonlinear dynamical perspective on model error: a proposal for non-local stochastic-dynamic parameterization in weather and climate prediction models. Q. J. R. Meteorol. Soc., 279– 304. - Powell
, M.. The NEWUOA software for unconstrained optimization without derivatives. In: Large-Scale Nonlinear Optimization. Springer, Boston, MA, pp. 255– 297. - Pulido
, M.and Rosso, O.. Model selection: using information measures from ordinal symbolic analysis to select model sub-grid scale parameterizations. J. Atmos. Sci., 3253– 3269. - Pulido
, M., Scheffler, G., Ruiz, J., Lucini, M.and Tandeo, P.. Estimation of the functional form of subgrid-scale schemes using ensemble-based data assimilation: a simple model experiment. Q. J. R. Meteorol. Soc., 2974– 2984. - Raanes
, P.. On the ensemble Rauch-Tung-Striebel smoother and its equivalence to the ensemble Kalman smoother. Q. J. R. Meteorol. Soc., 1259– 1264. - Ruiz
, J., Pulido, M.and Miyoshi, T.. Estimating parameters with ensemble-based data assimilation a review. J. Meteorol. Soc. Jpn., 79– 99. - Ruiz
, J., Pulido, M.and Miyoshi, T.. Estimating parameters with ensemble-based data assimilation parameter covariance treatment. J. Meteorol. Soc. Jpn., 453– 469. - Santitissadeekorn
, N.and Jones, C.. Two-stage filtering for joint state-parameter estimation. Mon. Wea. Rev., 2028– 2042. - Shaman
, J., Karspeck, A., Yang, W., Tamerius, J.and Lipsitch, M.. Real-time influenza forecasts during the 2012–2013 season. Nat. Commun., 2837. - Shumway
, R.and Stoffer, D.. An approach to time series smoothing and forecasting using the EM algorithm. J. Time Ser. Anal., 253– 264. - Shutts
, G.. A stochastic convective backscatter scheme for use in ensemble prediction systems. Q. J. R. Meteorol. Soc. 141, 2602– 2616. - Stensrud
, D.. Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, Cambridge. - Tandeo
, P., Pulido, M.and Lott, F.. Offline estimation of subgrid-scale orographic parameters using EnKF and maximum likelihood error covariance estimates. Q. J. R. Meteorol. Soc., 383– 395. - van Leeuwen
, P. J.. Particle filtering in geophysical systems. Mon. Wea. Rev., 4089– 4114. - Wei
, G.and Tanner, M. A.. A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. J. Amer. Stat. Assoc., 699– 704. - West
, M.and Liu, J.. Combined parameter and state estimation in simulation-based filtering. In: Sequential Monte Carlo Methods in Practice. Springer, New York, pp. 197– 223. - Wikle
, C.and Berliner, L.. A Bayesian tutorial for data assimilation. Physica D, 1– 16. - Wilks
, D. S.. Effects of stochastic parametrizations in the Lorenz 96 system. Q. J. R. Meteorol. Soc., 389– 407. - Wu
, C.. On the convergence properties of the EM algorithm. Ann. Stat. 11, 95– 103.
DOI: https://doi.org/10.1080/16000870.2018.1442099 | Journal eISSN: 3035-9554
Language: English
Page range: 1442099 - 1442099
Submitted on: Sep 21, 2017
Accepted on: Feb 9, 2018
Published on: Jan 1, 2018
Published by: Stockholm University Press
In partnership with: Paradigm Publishing Services
Keywords:
© 2018 Manuel Pulido, Pierre Tandeo, Marc Bocquet, Alberto Carrassi, Magdalena Lucini, published by Stockholm University Press
This work is licensed under the Creative Commons Attribution 4.0 License.