Bickel, P., Li, B., and Bengtsson, T.: Sharp failure rates for the bootstrap particle filter in high dimensions, in: Pushing the limits of contemporary statistics: Contributions in honor of Jayanta K. Ghosh, 318–329, Institute of Mathematical Statistics, 2008. a
Buizza, C., Quilodrán Casas, C., Nadler, P., Mack, J., Marrone, S., Titus, Z., Le Cornec, C., Heylen, E., Dur, T., Baca Ruiz, L., Heaney, C., Díaz Lopez, J. A., Kumar, K. S., and Arcucci, R.: Data Learning: Integrating Data Assimilation and Machine Learning, J. Comput. Sci., 58, 101525,
https://doi.org/10.1016/j.jocs.2021.101525, 2022.
a
Byrne, S., Wilcox, L. C., and Churavy, V.: MPI.jl: Julia bindings for the Message Passing Interface, Proceedings of the JuliaCon Conferences, 1, 68,
https://doi.org/10.21105/jcon.00068, 2021.
a,
b
Carlson, F. B., Roy, P., and Lu, Y.: LowLevelParticleFilters.jl: State estimation, smoothing and parameter estimation using Kalman and particle filters,
https://github.com/baggepinnen/LowLevelParticleFilters.jl (last access: 27 February 2023), 2018.
a,
b
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, Wiley Interdisciplinary Reviews: Climate Change, 9, e535,
https://doi.org/10.1002/wcc.535, 2018.
a
Chan, T. F., Golub, G. H., and LeVeque, R. J.: Updating formulae and a pairwise algorithm for computing sample variances, in: COMPSTAT 1982 5th Symposium held at Toulouse 1982: Part I: Proceedings in Computational Statistics, 30–41, Springer, 1982. a
Churavy, V., Godoy, W. F., Bauer, C., Ranocha, H., Schlottke-Lakemper, M., Räss, L., Blaschke, J., Giordano, M., Schnetter, E., Omlin, S., Vetter, J. S., and Edelman, A.: Bridging HPC Communities through the Julia Programming Language, arXiv e-prints,
https://doi.org/10.48550/arXiv.2211.02740, 2022.
a
Cotter, C., Crisan, D., Holm, D., Pan, W., and Shevchenko, I.: Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise, J. Stat. Phys., 179, 1186–1221, 2020. a
Dietrich, C. R. and Newsam, G. N.: Fast and exact simulation of stationary Gaussian processes through circulant embedding of the covariance matrix, SIAM J. Sci. Comput., 18, 1088–1107, 1997. a
Douc, R. and Cappé, O.: Comparison of resampling schemes for particle filtering, in: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 64–69, IEEE, 2005. a
Doucet, A., Godsill, S., and Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering, Stat. Comput., 10, 197–208,
https://doi.org/10.1023/A:1008935410038, 2000.
a,
b,
c
Dunbar, O. R. A., Lopez-Gomez, I., Garbuno-Iñigo, A., Huang, D. Z., Bach, E., and long Wu, J.: EnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibration, J. Open Source Softw., 7, 4869,
https://doi.org/10.21105/joss.04869, 2022.
a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, 1994. a
Evensen, G., Vossepoel, F. C., and van Leeuwen, P. J.: Fully Nonlinear Data Assimilation, 95–110, Springer International Publishing, Cham, ISBN 978-3-030-96709-3,
https://doi.org/10.1007/978-3-030-96709-3_9, 2022.
a
Foreman-Mackey, D., Agol, E., Ambikasaran, S., and Angus, R.: Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series, Astronom. J., 154, 220,
https://doi.org/10.3847/1538-3881/aa9332, 2017.
a
Gailler, A., Hébert, H., Loevenbruck, A., and Hernandez, B.: Simulation systems for tsunami wave propagation forecasting within the French tsunami warning center, Nat. Hazards Earth Syst. Sci., 13, 2465–2482,
https://doi.org/10.5194/nhess-13-2465-2013, 2013.
a
Giordano, M., Klöwer, M., and Churavy, V.: Productivity meets Performance: Julia on A64FX, in: 2022 IEEE International Conference on Cluster Computing (CLUSTER), 549–555,
https://doi.org/10.1109/CLUSTER51413.2022.00072, 2022.
a,
b
Gordon, N. J., Salmond, D. J., and Smith, A. F.: Novel approach to nonlinear/non-Gaussian Bayesian state estimation, in: IEE Proceedings F (Radar and Signal Processing), IET, 140, 107–113, 1993. a
Goto, C.: Equations of nonlinear dispersive long waves for a large Ursell number, Doboku Gakkai Ronbunshu, 1984, 193–201, 1984. a
Grudzien, C., Merchant, C., and Sandhu, S.: DataAssimilationBenchmarks.jl: a data assimilation research framework., J. Open Source Softw., 7, 4129,
https://doi.org/10.21105/joss.04129, 2022.
a
Gusman, A. R., Sheehan, A. F., Satake, K., Heidarzadeh, M., Mulia, I. E., and Maeda, T.: Tsunami data assimilation of Cascadia seafloor pressure gauge records from the 2012 Haida Gwaii earthquake, Geophys. Res. Lett., 43, 4189–4196,
https://doi.org/10.1002/2016GL068368, 2016.
a,
b
Jordán, A., Eyheramendy, S., and Buchner, J.: State-space Representation of Matérn and Damped Simple Harmonic Oscillator Gaussian Processes, Research Notes of the AAS, 5, 107,
https://doi.org/10.3847/2515-5172/abfe68, 2021.
a
Kondo, K. and Miyoshi, T.: Non-Gaussian statistics in global atmospheric dynamics: a study with a 10 240-member ensemble Kalman filter using an intermediate atmospheric general circulation model, Nonlin. Processes Geophys., 26, 211–225,
https://doi.org/10.5194/npg-26-211-2019, 2019.
a,
b
Koskela, T., Giordano, M., Graham, M., and Giles, D.: Team-RADDISH/ParticleDA.jl: v1.1.0 (v1.1.0), Zenodo [code],
https://doi.org/10.5281/zenodo.10814467, 2024.
a
Lee, A. and Piibeleht, M.: SequentialMonteCarlo.jl: A light interface to serial and multi-threaded Sequential Monte Carlo,
https://github.com/awllee/SequentialMonteCarlo.jl (last access: 27 February 2023), 2017.
a,
b
Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich, S.: Particle filters for high‐dimensional geoscience applications: A review, Q. J. Roy. Meteor. Soc., 145, 2335–2365,
https://doi.org/10.1002/qj.3551, 2019.
a,
b
Le Provost, M.: EnKF.jl: A framework for data assimilation with ensemble Kalman filter,
https://github.com/mleprovost/EnKF.jl (last access: 27 February 2023), 2016.
a,
b
Maeda, T., Obara, K., Shinohara, M., Kanazawa, T., and Uehira, K.: Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real-time tsunami forecasting, Geophys. Res. Lett., 42, 7923–7932,
https://doi.org/10.1002/2015GL065588, 2015.
a
Miyoshi, T.: Ensemble Kalman Filter Experiments with a Primitive-equation Global Model, Ph.D. thesis, University of Maryland, College Park, 2005.
a,
b,
c
Miyoshi, T., Kondo, K., and Imamura, T.: The 10,240-member ensemble Kalman filtering with an intermediate AGCM, Geophys. Res. Lett., 41, 5264–5271,
https://doi.org/10.1002/2014GL060863, 2014.
a,
b
Molteni, F.: Atmospheric simulations using a GCM with simplified physical parametrizations. I: Model climatology and variability in multi-decadal experiments, Clim. Dynam., 20, 175–191,
https://doi.org/10.1007/s00382-002-0268-2, 2003.
a,
b,
c
Nadler, P., Arcucci, R., and Guo, Y. K.: Data assimilation for parameter estimation in economic modelling, Proceedings – 15th International Conference on Signal Image Technology and Internet Based Systems, SISITS 2019, 649–656,
https://doi.org/10.1109/SITIS.2019.00106, 2019.
a
Rackauckas, C. and Nie, Q.: DifferentialEquations.jl – a performant and feature-rich ecosystem for solving differential equations in Julia, J. Open Res. Softw., 5, 15,
https://doi.org/10.5334/jors.151, 2017.
a
Robbe, P.: GaussianRandomFields.jl: A package for Gaussian random field generation in Julia, GitHub [code],
https://github.com/PieterjanRobbe/GaussianRandomFields.jl (last access: 31 August 2023), 2017. a
Ruzayqat, H., Er-Raiy, A., Beskos, A., Crisan, D., Jasra, A., and Kantas, N.: A lagged particle filter for stable filtering of certain high-dimensional state-space models, SIAM/ASA Journal on Uncertainty Quantification, 10, 1130–1161, 2022. a
Sanpei, A., Okamoto, T., Masamune, S., and Kuroe, Y.: A data-assimilation based method for equilibrium reconstruction of magnetic fusion plasma and its application to reversed field pinch, IEEE Access, 9, 74739–74751, 2021. a
Schauer, M., Gagnon, Y. L., St-Jean, C., and Cook, J.: Kalman.jl: Flexible filtering and smoothing in Julia,
https://github.com/mschauer/Kalman.jl (last access: 27 February 2023), 2018.
a,
b
Schoenbrod, S.: KalmanFilters.jl,
https://github.com/JuliaGNSS/KalmanFilters.jl (last access: 27 February 2023), 2018.
a,
b
Snyder, C.: Particle filters, the “optimal” proposal and high-dimensional systems, Proceedings of the ECMWF Seminar on Data Assimilation for Atmosphere and Ocean, 6–9,
http://www2.mmm.ucar.edu/people/snyder/papers/Snyder_ECMWFSem2011.pdf (last access: 27 February 2023), 2011.
a,
b,
c
Sunberg, Z., Lasse, P., Bouton, M., Fischer, J., Becker, T., Saba, E., Moss, R., Gupta, J. K., Dressel, L., Kelman, T., Wu, C., and Thibaut, L.: ParticleFilters.jl: Simple particle filter implementation in Julia,
https://github.com/JuliaPOMDP/ParticleFilters.jl (last access: 27 February 2023), 2017.
a,
b
Thépart, J.-N., Vasiljevic, D., Courtier, P., and Pailleux, J.: Variational assimilation of conventional meteorological observations with a multilevel primitive-equation model., Q. J. Roy. Meteor. Soc., 119, 153–186,
https://doi.org/10.1002/qj.49711950907, 1993.
a
Vetra-Carvalho, S., van Leeuwen, P. J., Nerger, L., Barth, A., Altaf, M. U., Brasseur, P., Kirchgessner, P., and Beckers, J. M.: State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems, Tellus A, 70, 1–38,
https://doi.org/10.1080/16000870.2018.1445364, 2018.
a,
b
