The DOI matches its OpenAlex record; 19 of the 19 references it cites were found in a registry or an index.
Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects
Open accessAbstract
Two general algorithms for solving constrained minimization problems are presented and discussed in the context of analysis and assimilation of meteorological observations. In both algorithms, the original constrained problem is transformed by appropriate modifications into one unconstrained problem, or into a sequence of unconstrained problems. The main advantage of proceeding in this way is that the new unconstrained problems can be solved by classical descent algorithms, thus avoiding the need of directly solving the Euler-Lagrange equations of the original constrained problem.The first algorithm presented in the augmented lagrangian algorithm. It generalizes the more classical penalty and duality algorithms. The second algorithm, inspired from optimal control techniques, is based on an appropriate use of an adjoint dynamical equation, and seems to be particularly well adapted to the assimilation of observations distributed in time. Simple numerical examples show the ability of these algorithms to solve non-linear minimization problems of the type encountered in meteorology. Their possible use in more complex situations is discussed, in particular in terms of their computational cost.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Methods of Mathematical Physics
Courant, R., & Hilbert, D. (1989) · Wiley eBooks · doi:10.1002/9783527617210 · Cited by 8,067
Levary, R. R. (1982) · European Journal of Operational Research · doi:10.1016/0377-2217(82)90093-5 · Cited by 4,158
Constrained Optimization and Lagrange Multiplier Methods
(1982) · Elsevier eBooks · doi:10.1016/c2013-0-10366-2 · Cited by 4,016
Optimal Control of Systems Governed by Partial Differential Equations.
Russell, D. L., Lions, J. L., & Mitter, S. K. (1972) · The American Mathematical Monthly · doi:10.2307/2318095 · Cited by 2,922
Optimal Control of Systems Governed by Partial Differential Equations
Lions, J. L. (1971) · Springer eBooks · doi:10.1007/978-3-642-65024-6 · Cited by 2,721
AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM
CRESSMAN, G. P. (1959) · Monthly Weather Review · doi:10.1175/1520-0493(1959)087<0367:aooas>2.0.co;2 · Cited by 1,798
A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
Kimeldorf, G. S., & Wahba, G. (1970) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177697089 · Cited by 939
Studies in Linear and Non-Linear Programming.
Beale, E. M. L., Arrow, K. J., Hurwicz, L., Uzawa, H., Chenery, H. B., Johnson, S. M., Karlin, S., Marschak, T., & Solow, R. M. (1959) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2342801 · Cited by 915
Methods of Mathematical Physics: Partial Differential Equations
Courant, R., & Hilbert, D. (1989) · doi:10.1002/9783527617234 · Cited by 735
A Global Three-Dimensional Multivariate Statistical Interpolation Scheme
Lorenc, A. C. (1981) · Monthly Weather Review · doi:10.1175/1520-0493(1981)109<0701:agtdms>2.0.co;2 · Cited by 562
Cited by
Atmospheric Modeling, Data Assimilation and Predictability
Kalnay, E. (2002) · doi:10.1017/cbo9780511802270 · Cited by 3,391
A framework for automated PDE-constrained optimisation
Funke, S. W., & Farrell, P. E. (2013) · arXiv · doi:10.48550/arxiv.1302.3894 · Cited by 58
Song, H., Miller, A. J., McClatchie, S., Weber, E. D., Nieto, K. M., & Checkley, D. M. (2012) · Journal of Geophysical Research: Oceans · doi:10.1029/2011jc007302 · Cited by 50
Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning
Hammoud, M. A. E. R., Raboudi, N., Titi, E. S., Knio, O., & Hoteit, I. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms004178 · Cited by 11
Metadata
- Authors
- Dimet, F. L., & Talagrand, O.
- Venue
- Tellus A Dynamic Meteorology and Oceanography
- Publisher
- Wiley
- Published
- 1986-01-01
- Volume
- 38
- Issue
- 2
- Pages
- 97-110
- ISSN
- 0280-6495 (ISSN-L)
- OpenAlex
- W2078031695
- Cited by
- 1,638
- References
- 19
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. https://app.cochat.ai/papers/10.1111/j.1600-0870.1986.tb00459.x
- The paper
- Dimet, F. L., & Talagrand, O. (1986). Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus A Dynamic Meteorology and Oceanography. https://doi.org/10.1111/j.1600-0870.1986.tb00459.x
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.
