Articles | Volume 22, issue 1
https://doi.org/10.5194/npg-22-15-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-22-15-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Review article
| 
13 Jan 2015
Review article |
| 13 Jan 2015
Toward the assimilation of images
F.-X. Le Dimet
CORRESPONDING AUTHOR
INRIA, LJK and Université de Grenoble Alpes, 38041, Grenoble CEDEX, France
I. Souopgui
Department of Marine Science, The University of Southern Mississippi, 1020 Balch Blvd, Stennis Space Center, MS 39529, USA
O. Titaud
CERFACS, 31057 Toulouse CEDEX 01, France
V. Shutyaev
Institute of Numerical Mathematics, Russian Academy of Sciences, 119333 Gubkina 8, Moscow, Russia
M. Y. Hussaini
Mathematics and Computational Science & Engineering, Florida State University, Tallahassee, Florida, 32306, USA
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Cited
16 citations as recorded by crossref.
- Development and preliminary validation of an EnKF-like image assimilation system for the Common Land Model X. Bai et al. https://doi.org/10.5194/gmd-19-4977-2026
- Numerical algorithm for morphogen synthesis region identification with indirect image-type measurement data A. Penenko et al. https://doi.org/10.1142/S021972001940002X
- ISANet: Deep Neural Network Approximating Image Sequence Assimilation for Tracking Fluid Flows L. Li & J. Ma https://doi.org/10.1109/TGRS.2023.3334612
- Source Identification for the Smoluchowski Equation Using an Ensemble of Adjoint Equation Solutions A. Penenko & A. Salimova https://doi.org/10.1134/S1995423920020068
- Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems A. Penenko et al. https://doi.org/10.3390/atmos12121697
- Traditional Data Assimilation and Its Effective Integration with Meteorological Deep Learning X. Zou https://doi.org/10.1016/j.eng.2025.11.023
- A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data A. Penenko https://doi.org/10.1134/S1995423919010051
- Unsteady flow enhancement on an airfoil using sliding window weak-constraint four-dimensional variational data assimilation S. Li et al. https://doi.org/10.1063/5.0152348
- Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko https://doi.org/10.1088/1755-1315/211/1/012052
- Data Assimilation Algorithms for Atmospheric Chemistry Models A. Penenko et al. https://doi.org/10.1134/S0001433825700719
- Feature preserving data assimilation via feature alignment A. Subrahmanya & A. Sandu https://doi.org/10.1016/j.cma.2025.118345
- Convergence analysis of the adjoint ensemble method in inverse source problems for advection-diffusion-reaction models with image-type measurements A. Penenko https://doi.org/10.3934/ipi.2020035
- Development and preliminary validation of a land surface image assimilation system based on the Common Land Model W. Shen et al. https://doi.org/10.5194/gmd-17-3447-2024
- An Algorithm for Identifying Pollution Sources with Non-Linear Measurement Operators A. Penenko et al. https://doi.org/10.1134/S1024856025701003
- Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers A. Penenko & E. Rusin https://doi.org/10.3390/math10234522
- Assimilation of Images via Dictionary Learning-Based Sparsity Regularization Strategy: An Application for Retrieving Fluid Flows L. Li et al. https://doi.org/10.1109/TGRS.2021.3110799
16 citations as recorded by crossref.
- Development and preliminary validation of an EnKF-like image assimilation system for the Common Land Model X. Bai et al. https://doi.org/10.5194/gmd-19-4977-2026
- Numerical algorithm for morphogen synthesis region identification with indirect image-type measurement data A. Penenko et al. https://doi.org/10.1142/S021972001940002X
- ISANet: Deep Neural Network Approximating Image Sequence Assimilation for Tracking Fluid Flows L. Li & J. Ma https://doi.org/10.1109/TGRS.2023.3334612
- Source Identification for the Smoluchowski Equation Using an Ensemble of Adjoint Equation Solutions A. Penenko & A. Salimova https://doi.org/10.1134/S1995423920020068
- Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems A. Penenko et al. https://doi.org/10.3390/atmos12121697
- Traditional Data Assimilation and Its Effective Integration with Meteorological Deep Learning X. Zou https://doi.org/10.1016/j.eng.2025.11.023
- A Newton–Kantorovich Method in Inverse Source Problems for Production-Destruction Models with Time Series-Type Measurement Data A. Penenko https://doi.org/10.1134/S1995423919010051
- Unsteady flow enhancement on an airfoil using sliding window weak-constraint four-dimensional variational data assimilation S. Li et al. https://doi.org/10.1063/5.0152348
- Algorithms for the inverse modelling of transport and transformation of atmospheric pollutants A. Penenko https://doi.org/10.1088/1755-1315/211/1/012052
- Data Assimilation Algorithms for Atmospheric Chemistry Models A. Penenko et al. https://doi.org/10.1134/S0001433825700719
- Feature preserving data assimilation via feature alignment A. Subrahmanya & A. Sandu https://doi.org/10.1016/j.cma.2025.118345
- Convergence analysis of the adjoint ensemble method in inverse source problems for advection-diffusion-reaction models with image-type measurements A. Penenko https://doi.org/10.3934/ipi.2020035
- Development and preliminary validation of a land surface image assimilation system based on the Common Land Model W. Shen et al. https://doi.org/10.5194/gmd-17-3447-2024
- An Algorithm for Identifying Pollution Sources with Non-Linear Measurement Operators A. Penenko et al. https://doi.org/10.1134/S1024856025701003
- Parallel Implementation of a Sensitivity Operator-Based Source Identification Algorithm for Distributed Memory Computers A. Penenko & E. Rusin https://doi.org/10.3390/math10234522
- Assimilation of Images via Dictionary Learning-Based Sparsity Regularization Strategy: An Application for Retrieving Fluid Flows L. Li et al. https://doi.org/10.1109/TGRS.2021.3110799
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