Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-5957-2021
https://doi.org/10.5194/gmd-14-5957-2021
Model description paper
 | 
04 Oct 2021
Model description paper |  | 04 Oct 2021

SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics

Olivier Pannekoucke and Philippe Arbogast

Related authors

HyPhAI v1.0: Hybrid Physics-AI architecture for cloud cover nowcasting
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
EGUsphere, https://doi.org/10.5194/egusphere-2023-3078,https://doi.org/10.5194/egusphere-2023-3078, 2024
Short summary
Toward a multivariate formulation of the parametric Kalman filter assimilation: application to a simplified chemical transport model
Antoine Perrot, Olivier Pannekoucke, and Vincent Guidard
Nonlin. Processes Geophys., 30, 139–166, https://doi.org/10.5194/npg-30-139-2023,https://doi.org/10.5194/npg-30-139-2023, 2023
Short summary
A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective
Olivier Pannekoucke, Richard Ménard, Mohammad El Aabaribaoune, and Matthieu Plu
Nonlin. Processes Geophys., 28, 1–22, https://doi.org/10.5194/npg-28-1-2021,https://doi.org/10.5194/npg-28-1-2021, 2021
Short summary
PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations
Olivier Pannekoucke and Ronan Fablet
Geosci. Model Dev., 13, 3373–3382, https://doi.org/10.5194/gmd-13-3373-2020,https://doi.org/10.5194/gmd-13-3373-2020, 2020
Short summary
Parametric covariance dynamics for the nonlinear diffusive Burgers equation
Olivier Pannekoucke, Marc Bocquet, and Richard Ménard
Nonlin. Processes Geophys., 25, 481–495, https://doi.org/10.5194/npg-25-481-2018,https://doi.org/10.5194/npg-25-481-2018, 2018
Short summary

Related subject area

Numerical methods
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024,https://doi.org/10.5194/gmd-17-3467-2024, 2024
Short summary
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024,https://doi.org/10.5194/gmd-17-3447-2024, 2024
Short summary
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024,https://doi.org/10.5194/gmd-17-3175-2024, 2024
Short summary
ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024,https://doi.org/10.5194/gmd-17-2427-2024, 2024
Short summary
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3–Cl–H2O system based on ISORROPIA II
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024,https://doi.org/10.5194/gmd-17-2197-2024, 2024
Short summary

Cited articles

Auer, M., Tschurtschenthaler, T., and Biffl, S.: A Flyweight UML Modelling Tool for Software Development in Heterogeneous Environments, in: Proceedings of the 29th Conference on EUROMICRO, EUROMICRO '03, pp. 267–272​​​​​​​, IEEE Computer Society, Washington, DC, USA, 1–6 September 2003, https://doi.org/10.1109/EURMIC.2003.1231600​​​​​​​, 2003. a
Berre, L.: Estimation of Synoptic and Mesoscale Forecast Error Covariances in a Limited-Area Model, Mon. Weather Rev., 128, 644–667, 2000. a
Bird, R. B. and Wiest, J. M.: Constitutive Equations for Polymeric Liquids, Annu. Rev. Fluid Mech., 27, 169–193, https://doi.org/10.1146/annurev.fl.27.010195.001125, 1995. a
Cohn, S.: Dynamics of Short-Term Univariate Forecast Error Covariances, Mon. Weather Rev., 121, 3123–3149, https://doi.org/10.1175/1520-0493(1993)121<3123:DOSTUF>2.0.CO;2, 1993. a, b
Courtier, P., Andersson, E., Pailleux, J., Vasiljević, W. H., Hamrud, D., Hollingsworth, M., Rabier, A. F., and Fisher, M.​​​​​​​​​​​​​​: The ECMWF implementation of three-dimensional variational assimilation (3D-Var). I: Formulation, Q. J. Roy. Meteor. Soc., 124, 1783–1807, 1998. a
Download
Short summary
This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.