Remote Sensing Technology and Application, Volume. 39, Issue 2, 381(2024)

Data-driven Data Assimilation Method based on Support Vector Machine Algorithm

Qinghe YU*, Yulong BAI, and Manhong FAN
Author Affiliations
  • College of Physics and Electronic Engineering,Northwest Normal University,Lanzhou 730070,China
  • show less
    References(46)

    [1] Xin LI, Feng LIU, Miao FANG. Harmonies between model and observation: Data assimilation in Earth System science. Science China earth Sciences, 50, 1185-1194(2020).

    [2] J J RUIZ, M PULIDO, T MIYOSHI. Estimating model parameters with ensemble-based data assimilation:A review. Journal of the Meteorological Society of Japan. Ser. II, 91, 79-99(2013).

    [3] J J RUIZ, C SAULO, J NOGUÉS-PAEGLE. WRF model sensitivity to choice of parameterization over South America: validation against surface variables. Monthly Weather Review, 138, 3342-3355(2010).

    [4] F LOTT, M J MILLER. A new subgrid‐scale orographic drag parametrization: Its formulation and testing. Quarterly Journal of the Royal Meteorological Society, 123, 101-127(1997).

    [5] X LUO, T BHAKTA, M JAKOBSEN et al. Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering. PloS One, 13(2018).

    [6] T MIYOSHI, M KUNII, J RUIZ et al. Big data assimilation” revolutionizing severe weather prediction. Bulletin of the American Meteorological Society, 97, 1347-1354(2016).

    [7] R V SOARES, X Luo, G EVENSEN et al. Handling big models and big data sets in history-matching problems through an adaptive local analysis scheme. SPE Journal, 26, 973-992(2021).

    [8] R LGUENSAT, P TANDEO, P AILLIOT et al. The analog data assimilation. Monthly Weather Review, 145, 4093-4107(2017).

    [9] D TRAN, L BOURDEV, R FERGUS et al. Learning spatiotemporal features with 3d convolutional networks, 4489-4497(2015).

    [10] M REICHSTEIN, G CAMPS-VALLS, B STEVENS et al. Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195-204(2019).

    [11] R FABLET, S OUALA, C HERZET. Bilinear residual neural network for the identification and forecasting of dynamical systems, 1-5(2017).

    [12] A M SCHÄFER, H G ZIMMERMANN. Recurrent neural networks are universal approximators, 632-640(2006).

    [13] J BRAJARD, A CARRASSI, M BOCQUET et al. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Comput Sci, 44, 1877-7503(2020).

    [14] M BOCQUET, J BRAJARD, A CARRASSI et al. Data assimilation as a deep learning tool to infer ODE representations of dynamical models. Nonlin Process Geophys, 26, 143-162(2019).

    [15] J PATHAK, B HUNT, M GIRVAN et al. Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters, 120(2018).

    [16] R FABLET, S OUALA, C HERZET. Bilinear residual neural network for the identification and forecasting of geophysical dynamics, 1477-1481(2018).

    [17] H O PARK, A DIBAZAR, T W BERGER. Discrete Synapse Recurrent Neural Network for nonlinear system modeling and its application on seismic signal classification, 1-7(2010).

    [18] H CHANG, D ZHANG. Identification of physical processes via combined data-driven and data-assimilation methods. Journal of Computational Physics, 393, 337-350(2019).

    [19] M FAN, Y BAI, L WANG et al. Coupling the K-nearest neighbors and locally weighted linear regression with ensemble Kalman filter for data-driven data assimilation. Open Geosciences, 13, 1395-1413(2021).

    [20] T M HAMILL, J S WHITAKER. Probabilistic quantitative precipitation forecasts based on reforecast analogs: Theory and application. Monthly Weather Review, 134, 3209-3229(2006).

    [21] L DELLE MONACHE, T NIPEN, Y LIU et al. Kalman filter and analog schemes to postprocess numerical weather predictions. Monthly Weather Review, 139, 3554-3570(2011).

    [22] L DELLE MONACHE, F A ECKEL, D L RIFE et al. Probabilistic weather prediction with an analog ensemble. Monthly Weather Review, 141, 3498-3516(2013).

    [23] F HAMILTON, T BERRY, T SAUER. Ensemble Kalman filtering without a model. Physical Review X, 6(2016).

    [24] F HAMILTON, T BERRY, T SAUER. Predicting chaotic time series with a partial model. Physical Review E, 92(2015).

    [25] J PATHAK, B HUNT, M GIRVAN et al. Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach. Physical review letters, 120(2018).

    [26] R ARCUCCI, L MOUTIQ, Y K GUO. Neural assimilation, 155-168(2020).

    [27] M BOCQUET, C A PIRES, L WU. Beyond Gaussian statistical modeling in geophysical data assimilation. Monthly Weather Review, 138, 2997-3023(2010).

    [28] J E BALL, D T ANDERSON, SR C S CHAN. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community. Journal of applied remote sensing, 11(2017).

    [29] P TANDEO, P AILLIOT, J RUIZ et al. Combining analog method and ensemble data assimilation: application to the Lorenz-63 chaotic system. Machine learning and data mining approaches to climate science, 3-12(2015).

    [31] S SUTHAHARAN. Support vector machine, 207-235(2016).

    [33] G BURGERS, P J VAN LEEUWEN, G EVENSEN. Analysis scheme in the ensemble Kalman filter. Monthly weather review, 126, 1719-1724(1998).

    [34] T M CHIN, M J TURMON, J B JEWELL et al. An ensemble-based smoother with retrospectively updated weights for highly nonlinear systems. Monthly weather review, 135, 186-202(2007).

    [35] R N MILLER, M GHIL, F GAUTHIEZ. Advanced data assimilation in strongly nonlinear dynamical systems. Journal of Atmospheric Sciences, 51, 1037-1056(1994).

    [36] P J VAN LEEUWEN. Nonlinear data assimilation in geosciences: an extremely efficient particle filter. Quarterly Journal of the Royal Meteorological Society, 136, 1991-1999(2010).

    [37] J L ANDERSON. Localization and sampling error correction in ensemble Kalman filter data assimilation. Monthly Weather Review, 140, 2359-2371(2012).

    [38] I HOTEIT, X LUO, D T PHAM. Particle Kalman filtering: A nonlinear Bayesian framework for ensemble Kalman filters. Monthly weather review, 140, 528-542(2012).

    [39] T HIGUCHI. Embedding reality in a numerical simulation with data assimilation, 1-7(2011).

    [40] M REICHSTEIN, G CAMPS-VALLS, B STEVENS et al. Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195-204(2019).

    [41] A T TEWELDEBRHAN, J F BURKHART, T V SCHULER et al. Improving the informational value of MODIS fractional snow cover area using fuzzy logic based ensemble smoother data assimilation frameworks. Remote Sensing, 11, 28(2018).

    [43] S. AKBARI, P.H. DABAGHIAN, O.M. San. Blending machine learning and sequential data assimilation over latent spaces for surrogate modeling of Boussinesq systems. Physica D-Nonlinear Phenomena, 448, 17(2023).

    [44] M MAIMAITIJIANG, V SAGAN, P SIDIKE et al. Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599(2020).

    [45] M BOCQUET, J BRAJARD, A CARRASSI, L Bertino. Data assimilation as a deep learning tool to infer ODE representations of dynamical models. Nonlin Process Geophys, 26, 143-162(2019).

    [46] X LI, Z ZHAO, F LIU. Big data assimilation to improve the predictability of COVID-19. Geography and Sustainability, 1, 317-320(2020).

    Tools

    Get Citation

    Copy Citation Text

    Qinghe YU, Yulong BAI, Manhong FAN. Data-driven Data Assimilation Method based on Support Vector Machine Algorithm[J]. Remote Sensing Technology and Application, 2024, 39(2): 381

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Oct. 31, 2022

    Accepted: --

    Published Online: Aug. 13, 2024

    The Author Email: YU Qinghe (981754137@qq.com)

    DOI:10.11873/j.issn.1004-0323.2024.2.0381

    Topics