A reduced‐order approach to four‐dimensional variational data assimilation using proper orthogonal decomposition
By Yanhua Cao, Jiang Zhu, Ionel M. Navon, Zhendong Luo. 2007
207 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change, Oceanography
View Paper: https://www.semanticscholar.org/paper/64108497995cb03ea0991fedf2a1be26c624fff0
Citations in the corpus
- Non-linear Petrov-Galerkin methods for reduced order modelling of the Navier-Stokes equations using a mixed finite element pair (2013, unknown): “POD has been used successfully in several fields, such as fluid dynamics [9] [10] [11], signal processing and pattern recognition [3], inverse problems [12] [13] and ocean modelling and four-dimensional variational (4D-Var) data assimilation [14] [15] [16] [17].”
- Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, unknown): “[267] Y.”
- Non‐intrusive reduced‐order modelling of the Navier–Stokes equations based on RBF interpolation (2015, unknown): “[5], air pollution model in Fang et al.”
- Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation (2018, unknown): “We also believe that the error bounds can be gainfully applied in a multi-fidelity approach to solve the 4D-Var problem, e.g., in a trust-region approach as proposed in Chen et al. (2011) and Du et al. (2013).”
- Non-intrusive reduced order modelling of the Navier-Stokes equations (2015, unknown): “[5], Vermeulen and Heemink [6] and also to shallow water equation models.”
- A Dual-Weighted Approach to Order Reduction in 4DVAR Data Assimilation (2008, unknown): “For oceanic models, initial efforts on reduced-order 4DVAR were put forward by Blayo et al. (1998) and Durbiano (2001).”
- A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows (2009, unknown): “The practical utility of the POD approach was extended to large scale complex flow dynamics such as atmospheric and ocean modelling and the four-dimensional variational (4D-Var) data assimilation ([14, 15, 16, 7]).”