Adjoint sensitivity analysis of regional air quality models
By Adrian Sandu, Dacian N. Daescu, Gregory R. Carmichael, Tianfeng Chai. 2005
257 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/cb8ae3106d5dcceda3efe8eaef6edbff9df8d6fd
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In Atmospheric Science
In Environmental Engineering
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Citations in the corpus
- Data assimilation: making sense of Earth Observation (2014, methodology): “…of the background errors (Constantinescu et al., 2007a; Singh et al., 2011; Errera and Ménard, 2012); assessment of technical aspects of the chemical model, e.g., adjoint sensitivity (Sandu et al., 2003, 2005); and comparison of assimilation methods, e.g., 4D-Var vs. EnKF (Skachko et al., 2014).”
- Construction of non-diagonal background error covariance matrices for global chemical data assimilation (2010, background): “This quasi-Newton approach has become the “gold standard” in solving large scale chemical data assimilation problems (Sandu et al., 2005).”
- Universal Differential Equations for Scientific Machine Learning (2020, methodology): “[115] Adrian Sandu, Dacian N Daescu, Gregory R Carmichael, and Tianfeng Chai.”