Articles | Volume 25, issue 2
https://doi.org/10.5194/npg-25-355-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-25-355-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2018
Research article |
| 03 May 2018
| 03 May 2018
Feature-based data assimilation in geophysics
Matthias Morzfeld
CORRESPONDING AUTHOR
Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., P.O. Box 210089, Tucson, Arizona 85721, USA
Jesse Adams
Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., P.O. Box 210089, Tucson, Arizona 85721, USA
Spencer Lunderman
Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., P.O. Box 210089, Tucson, Arizona 85721, USA
Rafael Orozco
Department of Mathematics, University of Arizona, 617 N. Santa Rita Ave., P.O. Box 210089, Tucson, Arizona 85721, USA
Viewed
Total article views: 3,279 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Oct 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,940 | 1,157 | 182 | 3,279 | 202 | 183 |
- HTML: 1,940
- PDF: 1,157
- XML: 182
- Total: 3,279
- BibTeX: 202
- EndNote: 183
Total article views: 2,554 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 03 May 2018)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,591 | 802 | 161 | 2,554 | 180 | 155 |
- HTML: 1,591
- PDF: 802
- XML: 161
- Total: 2,554
- BibTeX: 180
- EndNote: 155
Total article views: 725 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 06 Oct 2017)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 349 | 355 | 21 | 725 | 22 | 28 |
- HTML: 349
- PDF: 355
- XML: 21
- Total: 725
- BibTeX: 22
- EndNote: 28
Viewed (geographical distribution)
Total article views: 3,279 (including HTML, PDF, and XML)
Thereof 3,041 with geography defined
and 238 with unknown origin.
Total article views: 2,554 (including HTML, PDF, and XML)
Thereof 2,347 with geography defined
and 207 with unknown origin.
Total article views: 725 (including HTML, PDF, and XML)
Thereof 694 with geography defined
and 31 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
17 citations as recorded by crossref.
- A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field M. Morzfeld & B. Buffett 10.5194/npg-26-123-2019
- Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck 10.1002/qj.4001
- An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo 10.1063/5.0083071
- A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity J. Lopez-Alvis et al. 10.1016/j.advwatres.2019.103427
- Calibrate, emulate, sample E. Cleary et al. 10.1016/j.jcp.2020.109716
- Model and data reduction for data assimilation: Particle filters employing projected forecasts and data with application to a shallow water model A. Albarakati et al. 10.1016/j.camwa.2021.05.026
- Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems A. Chattopadhyay et al. 10.2139/ssrn.4142015
- Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering J. Fiddes et al. 10.5194/hess-23-4717-2019
- Toward Utilizing Similarity in Hydrologic Data Assimilation H. Lee et al. 10.3390/hydrology11110177
- Efficient Bayesian inference for large chaotic dynamical systems S. Springer et al. 10.5194/gmd-14-4319-2021
- Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods O. Dunbar et al. 10.1137/21M1410853
- A testbed for geomagnetic data assimilation K. Gwirtz et al. 10.1093/gji/ggab327
- Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems A. Chattopadhyay et al. 10.1016/j.jcp.2023.111918
- Bayesian spatiotemporal modeling for inverse problems S. Lan et al. 10.1007/s11222-023-10253-z
- Rigorous convergence bounds for stochastic differential equations with application to uncertainty quantification L. Blake et al. 10.1016/j.physd.2025.134742
- Estimating parameters of the nonlinear cloud and rain equation from a large-eddy simulation S. Lunderman et al. 10.1016/j.physd.2020.132500
- Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods I. Lopez‐Gomez et al. 10.1029/2022MS003105
17 citations as recorded by crossref.
- A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field M. Morzfeld & B. Buffett 10.5194/npg-26-123-2019
- Particle filters for data assimilation based on reduced‐order data models J. Maclean & E. Van Vleck 10.1002/qj.4001
- An improved framework for the dynamic likelihood filtering approach to data assimilation D. Foster & J. Restrepo 10.1063/5.0083071
- A cross-validation framework to extract data features for reducing structural uncertainty in subsurface heterogeneity J. Lopez-Alvis et al. 10.1016/j.advwatres.2019.103427
- Calibrate, emulate, sample E. Cleary et al. 10.1016/j.jcp.2020.109716
- Model and data reduction for data assimilation: Particle filters employing projected forecasts and data with application to a shallow water model A. Albarakati et al. 10.1016/j.camwa.2021.05.026
- Deep Learning-Enhanced Ensemble-Based Data Assimilation for High-Dimensional Nonlinear Dynamical Systems A. Chattopadhyay et al. 10.2139/ssrn.4142015
- Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering J. Fiddes et al. 10.5194/hess-23-4717-2019
- Toward Utilizing Similarity in Hydrologic Data Assimilation H. Lee et al. 10.3390/hydrology11110177
- Efficient Bayesian inference for large chaotic dynamical systems S. Springer et al. 10.5194/gmd-14-4319-2021
- Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods O. Dunbar et al. 10.1137/21M1410853
- A testbed for geomagnetic data assimilation K. Gwirtz et al. 10.1093/gji/ggab327
- Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems A. Chattopadhyay et al. 10.1016/j.jcp.2023.111918
- Bayesian spatiotemporal modeling for inverse problems S. Lan et al. 10.1007/s11222-023-10253-z
- Rigorous convergence bounds for stochastic differential equations with application to uncertainty quantification L. Blake et al. 10.1016/j.physd.2025.134742
- Estimating parameters of the nonlinear cloud and rain equation from a large-eddy simulation S. Lunderman et al. 10.1016/j.physd.2020.132500
- Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods I. Lopez‐Gomez et al. 10.1029/2022MS003105
Latest update: 27 Jun 2025
Short summary
Many applications in science require that computational models and data be combined. In a Bayesian framework, this is usually done by defining likelihoods based on the mismatch of model outputs and data. However, matching model outputs and data in this way can be unnecessary or impossible. This issue can be addressed by selecting features of the data, and defining likelihoods based on the features, rather than by the usual mismatch of model output and data.
Many applications in science require that computational models and data be combined. In a...
