American Institute of Mathematical Sciences

Advanced
doi: 10.3934/fods.2020015

Online learning of both state and dynamics using ensemble Kalman filters

Marc Bocquet , , Alban Farchi and  Quentin Malartic

CEREA, joint laboratory École des Ponts ParisTech and EDF R & D, Université Paris-Est, Champs-sur-Marne, France

* Corresponding author: Marc Bocquet

Received  June 2020 Revised  August 2020 Published  September 2020

The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning. To deal with partial and noisy observations in that endeavor, machine learning representations of the surrogate model can be used within a Bayesian data assimilation framework. However, these approaches require to consider long time series of observational data, meant to be assimilated all together. This paper investigates the possibility to learn both the dynamics and the state online, i.e. to update their estimates at any time, in particular when new observations are acquired. The estimation is based on the ensemble Kalman filter (EnKF) family of algorithms using a rather simple representation for the surrogate model and state augmentation. We consider the implication of learning dynamics online through (ⅰ) a global EnKF, (ⅰ) a local EnKF and (ⅲ) an iterative EnKF and we discuss in each case issues and algorithmic solutions. We then demonstrate numerically the efficiency and assess the accuracy of these methods using one-dimensional, one-scale and two-scale chaotic Lorenz models.

Keywords: Data assimilation, machine learning, chaotic dynamical systems, parameter estimation, ensemble Kalman filter, local ensemble Kalman filter, iterative ensemble Kalman filter.
Mathematics Subject Classification: Primary: 62M20, 49M41; Secondary: 86-08.
Citation: Marc Bocquet, Alban Farchi, Quentin Malartic. Online learning of both state and dynamics using ensemble Kalman filters. Foundations of Data Science, doi: 10.3934/fods.2020015
References:
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show all references

References:
[1]

H. D. I. Abarbanel, P. J. Rozdeba and S. Shirman, Machine learning: Deepest learning as statistical data assimilation problems, Neural Computation, 30 (2018), 2025-2055.  doi: 10.1162/neco_a_01094.  Google Scholar

[2]

A. Aksoy, F. Zhang and J. Nielsen-Gammon, Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model, Mon. Wea. Rev., 134 (2006), 2951-2969.  doi: 10.1175/MWR3224.1.  Google Scholar

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T. Arcomano, I. Szunyogh, J. Pathak, A. Wikner, B. R. Hunt and E. Ott, A machine learning-based global atmospheric forecast model, Geophys. Res. Lett., 47 (2020), e2020GL087776. Google Scholar

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C. H. Bishop, B. J. Etherton and S. J. Majumdar, Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects, Mon. Wea. Rev., 129 (2001), 420-436.  doi: 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2.  Google Scholar

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C. H. Bishop, J. S. Whitaker and L. Lei, Gain form of the ensemble transform Kalman filter and its relevance to satellite data assimilation with model space ensemble covariance localization, Mon. Wea. Rev., 145 (2017), 4575-4592.  doi: 10.1175/MWR-D-17-0102.1.  Google Scholar

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C. M. Bishop, Training with noise is equivalent to Tikhonov regularization, Neural Computation, 7 (1995), 108-116.  doi: 10.1162/neco.1995.7.1.108.  Google Scholar

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M. Bocquet, Ensemble Kalman filtering without the intrinsic need for inflation, Nonlin. Processes Geophys., 18 (2011), 735-750.  doi: 10.5194/npg-18-735-2011.  Google Scholar

[8]

M. Bocquet, Localization and the iterative ensemble Kalman smoother, Q. J. R. Meteorol. Soc., 142 (2016), 1075-1089.  doi: 10.1002/qj.2711.  Google Scholar

[9]

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[10]

M. Bocquet, J. Brajard, A. Carrassi and L. Bertino, Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2 (2020), 55-80.  doi: 10.3934/fods.2020004.  Google Scholar

[11]

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[12]

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[13]

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[14]

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[15]

M. Bocquet and P. Sakov, Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20 (2013), 803-818.  doi: 10.5194/npg-20-803-2013.  Google Scholar

[16]

J. Brajard, A. Carrassi, M. Bocquet and L. Bertino, Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model, J. Comput. Sci., 44 (2020), 101171. doi: 10.1016/j.jocs.2020.101171.  Google Scholar

[17]

J. Brajard, A. Carrassi, M. Bocquet and L. Bertino, Combining data assimilation and machine learning to infer unresolved scale parametrisation, Philosophical Transactions A, 0 (2020), 0, Submitted, arXiv preprint: arXiv: 2009.04318. Google Scholar

[18]

S. L. Brunton, J. L. Proctor and J. N. Kutz, Discovering governing equations from data by sparse identification of nonlinear dynamical systems, PNAS, 113 (2016), 3932-3937.  doi: 10.1073/pnas.1517384113.  Google Scholar

[19]

M. Carlu, F. Ginelli, V. Lucarini and A. Politi, Lyapunov analysis of multiscale dynamics: The slow bundle of the two-scale Lorenz 96 model, Nonlin. Processes Geophys., 26 (2019), 73-89.  doi: 10.5194/npg-26-73-2019.  Google Scholar

[20]

A. Carrassi, M. Bocquet, L. Bertino and G. Evensen, Data assimilation in the geosciences: An overview on methods, issues, and perspectives, WIREs Climate Change, 9 (2018), e535. doi: 10.1002/wcc.535.  Google Scholar

[21]

C. L. Defforge, B. Carissimo, M. Bocquet, R. Bresson and P. Armand, Improving CFD atmospheric simulations at local scale for wind resource assessment using the iterative ensemble Kalman smoother, J. Wind. Eng. Ind. Aerod., 189 (2019), 243-257.  doi: 10.1016/j.jweia.2019.03.030.  Google Scholar

[22]

P. D. Dueben and P. Bauer, Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev., 11 (2018), 3999-4009.  doi: 10.5194/gmd-11-3999-2018.  Google Scholar

[23]

G. Evensen, Data Assimilation: The Ensemble Kalman Filter, 2$^nd$ edition, Springer-Verlag Berlin Heildelberg, 2009. doi: 10.1007/978-3-642-03711-5.  Google Scholar

[24]

R. Fablet, S. Ouala and C. Herzet, Bilinear residual neural network for the identification and forecasting of dynamical systems, in EUSIPCO 2018, European Signal Processing Conference, Rome, Italy, 2018, 1–5. Google Scholar

[25]

A. Farchi and M. Bocquet, On the efficiency of covariance localisation of the ensemble Kalman filter using augmented ensembles, Front. Appl. Math. Stat., 5 (2019), 3. doi: 10.3389/fams.2019.00003.  Google Scholar

[26]

E. Fertig, Observation bias correction with an ensemble Kalman filter, Tellus A, 61 (2009), 210-226.   Google Scholar

[27]

A. Fillion, M. Bocquet, S. Gratton, S. Gürol and P. Sakov, An iterative ensemble Kalman smoother in presence of additive model error, SIAM/ASA J. Uncertainty Quantification, 8 (2020), 198-228.  doi: 10.1137/19M1244147.  Google Scholar

[28]

G. Gaspari and S. E. Cohn, Construction of correlation functions in two and three dimensions, Q. J. R. Meteorol. Soc., 125 (1999), 723-757.  doi: 10.1002/qj.49712555417.  Google Scholar

[29]

C. Grudzien, A. Carrassi and M. Bocquet, Chaotic dynamics and the role of covariance inflation for reduced rank Kalman filters with model error, Nonlin. Processes Geophys., 25 (2018), 633-648.  doi: 10.5194/npg-25-633-2018.  Google Scholar

[30]

T. M. Hamill, J. S. Whitaker and C. Snyder, Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter, Mon. Wea. Rev., 129 (2001), 2776-2790.  doi: 10.1175/1520-0493(2001)129<2776:DDFOBE>2.0.CO;2.  Google Scholar

[31]

R. A. Horn and C. R. Johnson, Matrix Analysis, 2$^{nd}$ edition, Cambridge University Press, 2013.  Google Scholar

[32]

P. L. Houtekamer and H. L. Mitchell, A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Wea. Rev., 129 (2001), 123-137.  doi: 10.1175/1520-0493(2001)129<0123:ASEKFF>2.0.CO;2.  Google Scholar

[33]

W. W. Hsieh and B. Tang, Applying neural network models to prediction and data analysis in meteorology and oceanography, Bull. Amer. Meteor. Soc., 79 (1998), 1855-1870.  doi: 10.1175/1520-0477(1998)079<1855:ANNMTP>2.0.CO;2.  Google Scholar

[34]

X.-M. Hu, F. Zhang and J. W. Nielsen-Gammon, Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: A real-data study, Geophys. Res. Lett., 37 (2010), L08802. doi: 10.1029/2010GL043017.  Google Scholar

[35]

B. R. Hunt, E. J. Kostelich and I. Szunyogh, Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D, 230 (2007), 112-126.  doi: 10.1016/j.physd.2006.11.008.  Google Scholar

[36] A. H. Jazwinski, Stochastic Processes and Filtering Theory, Academic Press, New-York, 1970.   Google Scholar
[37]

N. B. Kovachki and A. M. Stuart, Ensemble Kalman inversion: A derivative-free technique for machine learning tasks, Inverse Problems, 35 (2019), 095005. doi: 10.1088/1361-6420/ab1c3a.  Google Scholar

[38]

H. Koyama and M. Watanabe, Reducing forecast errors due to model imperfections using ensemble Kalman filtering, Mon. Wea. Rev., 138 (2010), 3316-3332.  doi: 10.1175/2010MWR3067.1.  Google Scholar

[39]

R. Lguensat, P. Tandeo, P. Ailliot, M. Pulido and R. Fablet, The analog data assimilation, Mon. Wea. Rev., 145 (2017), 4093-4107.  doi: 10.1175/MWR-D-16-0441.1.  Google Scholar

[40]

Z. Long, Y. Lu, X. Ma and B. Dong, PDE-Net: Learning PDEs from data, in Proceedings of the 35th International Conference on Machine Learning, 2018. Google Scholar

[41]

E. N. Lorenz, Designing chaotic models, J. Atmos. Sci., 62 (2005), 1574-1587.  doi: 10.1175/JAS3430.1.  Google Scholar

[42]

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Figure 1.  Average state RMSE for the EnKF-ML with an hybrid adaptive inflation scheme, applied to the L96 model, for a range of ensemble size $ {N_\mathrm{e}} $ and several values of the standard deviations of the initial parameter vector mean. The error bars correspond to the standard deviation of $ {N_\mathrm{exp}} = 100 $ repeated experiments
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Figure 2.  Initial evolution of the $ {N_\mathrm{p}} = 18 $ parameters of the surrogate model learned on the observation of a L96 model run. The key parameters are the forcing ($ F = 8 $ in the true model), the friction ($ -1 $ in the true model) and the advection coefficients ($ -1, 1 $ in the true model)
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Figure 3.  Left panel (a): For both models, comparison of the performance of the EnKF-ML where the model is unknown and of the traditional EnKF where the model is known. The error bars are obtained from the standard deviation of $ {N_\mathrm{exp}} = 50 $ repeated experiments. Right panel (b): Comparison of Lyapunov spectra of the L96 model and of the surrogate model about an L96 trajectory
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Figure 4.  For both models, comparison of the performance of the LEnKF-ML where the model is unknown and of the traditional LEnKF where the model is known. The error bars are obtained from the standard deviation of $ {N_\mathrm{exp}} = 10 $ repeated experiments
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Figure 5.  Left panel (a): For both models, comparison of the performance of the LEnKF-ML where the model is unknown and of the traditional LEnKF where the model is known, as a function of the observation density. Right panel (b): For both models, comparison of the performance of the LEnKF-ML where the model is unknown and of the traditional LEnKF where the model is known, as a function of the observation error standard deviation. In all cases, the ensemble size is $ {N_\mathrm{e}} = 24 $ and the RMSE statistics are accumulated over $ {N_\mathrm{exp}} = 10 $ experiments
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Figure 6.  Left panel (a): Optimal tapering coefficient $ \zeta $ across a range of ensemble sizes $ {N_\mathrm{e}} $ for LEnKF-ML applied to the L96 model ($ {N_\mathrm{x}} = 40 $). Right panel (b): Optimal tapering coefficient $ \zeta $ across a range of model state space dimensions $ {N_\mathrm{x}} $ for LEnKF-ML applied to the L96 model, assuming $ {N_\mathrm{e}} = 40 $, a fixed multiplicative inflation and localization length
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Figure 7.  Comparison for both models of the performance of the IEnKF-ML where the model is unknown and of the traditional IEnKF where the model is known, as a function of the time interval between updates $ \Delta t $. The absence of a data point means that at least one of the $ {N_\mathrm{e}} = 10 $ DA runs was divergent
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