The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review
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In the citing paper
“Our results provide strong evidence for the disputed hypothesis37–39 that learning to predict short-term weather is an effective way to tune parameterizations for climate.”
From Neural general circulation models for weather and climate (2024) · cites this paper as [37]
Citations
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Evensen, G. (1994) · Journal of Geophysical Research: Oceans · doi:10.1029/94jc00572 · Cited by 5,371
On sequential Monte Carlo sampling methods for Bayesian filtering
Doucet, A., Godsill, S., & Andrieu, C. (2000) · Statistics and Computing · doi:10.1023/a:1008935410038 · Cited by 4,411
Atmospheric Modeling, Data Assimilation and Predictability
Kalnay, E. (2002) · doi:10.1017/cbo9780511802270 · Cited by 3,391 · cited 1×
“Ideally, for a perfect ensemble system, there should be a relationship between the ensemble spread and ensemble mean error (Kalnay 2003).”
BackgroundAn Ensemble Adjustment Kalman Filter for Data Assimilation
Anderson, J. G. (2001) · Monthly Weather Review · doi:10.1175/1520-0493(2001)129<2884:aeakff>2.0.co;2 · Cited by 1,749
Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter
Hunt, B. R., Kostelich, E. J., & Szunyogh, I. (2007) · Physica D: Nonlinear Phenomena · doi:10.1016/j.physd.2006.11.008 · Cited by 1,742
Ensemble Data Assimilation without Perturbed Observations
Whitaker, J. S., & Hamill, T. M. (2002) · Monthly Weather Review · doi:10.1175/1520-0493(2002)130<1913:edawpo>2.0.co;2 · Cited by 1,563
Uncertainty in predictions of the climate response to rising levels of greenhouse gases
Stainforth, D. A., Aina, T., Christensen, C., Collins, M., Faull, N., Frame, D. J., Kettleborough, J. A., Knight, S., Martin, A., Murphy, J. M., et al. (2005) · Nature · doi:10.1038/nature03301 · Cited by 1,166
Stochastic representation of model uncertainties in the ECMWF ensemble prediction system
Buizza, R., Milleer, M., & Palmer, T. N. (1999) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.49712556006 · Cited by 1,103
Anderson, J. G., & Anderson, S. L. (1999) · Monthly Weather Review · Cited by 1,042
Research works printed but not linked: 2
- Jazwinski, A. H., 1970: Stochastic and Filtering Theory. Mathematics in Sciences and Engineering Series. 64. Academic Press, 376 pp.
- Press, W. H., S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, 1992: Numerical Recipes in Fortran 77: The Art of Scientific Computing. 2nd Ed., Cambridge University Press, 933 pp.
Cited by
Neural general circulation models for weather and climate
Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., et al. (2024) · Nature · doi:10.1038/s41586-024-07744-y · Cited by 454
“Our results provide strong evidence for the disputed hypothesis 37–39 that learning to predict short-term weather is an effective way to tune parameterizations for climate.”
ResultSchneider, T., Lan, S., Stuart, A., & Teixeira, J. (2017) · Geophysical Research Letters · doi:10.1002/2017gl076101 · Cited by 391
Metadata
- Authors
- RUIZ, J. J., PULIDO, M., & MIYOSHI, T.
- Affiliations
- Department of Physics, FACENA, Universidad Nacional del Nordeste, Corrientes, Argentina, CIMA, UMI-IFAECI/CNRS, CONICET, Buenos Aires, Argentina
- Centre National de la Recherche Scientifique (FR)
- University of Maryland, College Park (US)
- Venue
- Journal of the Meteorological Society of Japan. Ser. II
- Published
- 2013
- Volume
- 91
- Issue
- 2
- Pages
- 79-99
- ISSN
- 0026-1165 (ISSN-L)
- 2186-9057
- OpenAlex
- W2010299042
- Cited by
- 145
- References
- 79
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 9 · 2023: 13 · 2022: 15 · 2021: 28 · 2020: 8 · 2019: 11 · 2018: 8 · 2017: 6 · 2016: 9 · 2015: 8 · 2014: 8 · 2013: 3
- FWCI
- 6.08
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Meteorological Society of Japan Ser II (publishedVersion)
- Americanae (AECID Library) (submittedVersion, cc-by-nc-sa)
- CONICET Digital (CONICET) (submittedVersion, cc-by-nc-sa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-sa)
- Funders
- Agencia Nacional de Promoción Científica y Tecnológica (PICT-2010-1986)
- Secretaría de Ciencia y Técnica, Universidad de Buenos Aires
- Topics
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Oceanographic and Atmospheric Processes
- Physical Sciences › Engineering › Aerospace Engineering › Aerospace and Aviation Technology
- Keywords
- model parameter estimation, ensemble data assimilation, SPEEDY model, atmospheric general circulation model, numerical weather prediction, parameter optimization, weather forecasting
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review. https://app.cochat.ai/papers/10.2151/jmsj.2013-201
- The paper
- RUIZ, J. J., PULIDO, M., & MIYOSHI, T. (2013). Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review. Journal of the Meteorological Society of Japan. Ser. II. https://doi.org/10.2151/jmsj.2013-201
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
