Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Dacian N. Daescu"'
Autor:
Dacian N. Daescu, Rolf H. Langland
Publikováno v:
Monthly Weather Review.
The forecast sensitivity to observations (FSO) is embedded into a new optimization framework for improving the observation performance in atmospheric data assimilation. Key ingredients are introduced as follows: the innovation-weight parametrization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8537fa4fcfa3a5d788f6aa0a7405e46b
https://doi.org/10.1175/mwr-d-21-0305.1
https://doi.org/10.1175/mwr-d-21-0305.1
Publikováno v:
Water Resources Research
This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter est
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1552da0fb8722e9ff2d8e22312ef9334
http://europepmc.org/articles/PMC6559328
http://europepmc.org/articles/PMC6559328
Autor:
Jeremy A. Shaw, Dacian N. Daescu
Publikováno v:
Journal of Computational Physics. 343:115-129
This article presents the mathematical framework to evaluate the sensitivity of a forecast error aspect to the input parameters of a weak-constraint four-dimensional variational data assimilation system (w4D-Var DAS), extending the established theory
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca7392988c322b9f29b6945ac1298596
https://doi.org/10.1016/j.jcp.2017.04.050
https://doi.org/10.1016/j.jcp.2017.04.050
Autor:
Rolf H. Langland, Dacian N. Daescu
Publikováno v:
IFAC-PapersOnLine. 49:176-181
An innovation-weight parametrization is introduced as a practical approach to account for deficiencies in the representation of both background error and observation error covariance in a variational data assimilation system. The adjoint-based evalua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::630de6dc73479d529b448d9b706cc6f8
https://doi.org/10.1016/j.ifacol.2016.10.159
https://doi.org/10.1016/j.ifacol.2016.10.159
Autor:
Dacian N. Daescu, Rolf H. Langland
Publikováno v:
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III) ISBN: 9783319434148
Novel applications of the adjoint-based sensitivity tools are investigated to obtain a priori guidance on the forecast impact of modeling correlated observational errors in a four-dimensional variational data assimilation system (4D-Var DAS) . A syne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f61e63927b1608b11da01f6585f0ef70
https://doi.org/10.1007/978-3-319-43415-5_16
https://doi.org/10.1007/978-3-319-43415-5_16
Autor:
Dacian N. Daescu, Rolf H. Langland
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 139:226-241
This article presents the adjoint-data assimilation system (adjoint-DAS) approach to evaluate the forecast sensitivity with respect to the specification of the observation-error covariance (R-sensitivity) and background-error covariance (B-sensitivit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::590da89ecd605115fd7d71c1dfa1b639
https://doi.org/10.1002/qj.1943
https://doi.org/10.1002/qj.1943
Publikováno v:
International Journal for Numerical Methods in Fluids. 69:110-123
SUMMARY An observation sensitivity (OS) method to identify targeted observations is implemented in the context of four-dimensional variational (4D-Var) data assimilation. This methodology is compared with the well-established adjoint sensitivity (AS)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c867c16b5c2e799bee016f6d31b4f6f4
https://doi.org/10.1002/fld.2545
https://doi.org/10.1002/fld.2545
Autor:
Humberto C. Godinez, Dacian N. Daescu
Publikováno v:
Computational Geosciences. 15:477-488
The efficiency of current adjoint-based observations targeting strategies in variational data assimilation is closely determined by the underlying assumption of a linear propagation of initial condition errors into the model forecasts. A novel target
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::023bc7539277c8193bbff85de127c56c
https://doi.org/10.1007/s10596-010-9217-z
https://doi.org/10.1007/s10596-010-9217-z
Autor:
Ricardo Todling, Dacian N. Daescu
Publikováno v:
Quarterly Journal of the Royal Meteorological Society. 136:2000-2012
The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjoint-DAS) makes feasible the evaluation of the local sensitivity of a model forecast aspect with respect to a large number of parameters in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5af0865749ab297ee2a4349769708cd
https://doi.org/10.1002/qj.693
https://doi.org/10.1002/qj.693
Autor:
Dacian N. Daescu
Publikováno v:
ICCS
The development of the adjoint of the forecast model and of the adjoint of the data assimilation system (adjointDAS) make feasible the evaluation of the derivative-based forecast sensitivity to DAS input parameters in numerical weather prediction (NW
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62887d4040239e125b74a7a6f068c778
https://doi.org/10.1016/j.procs.2010.04.142
https://doi.org/10.1016/j.procs.2010.04.142