Observation and background adjoint sensitivity in the adaptive observation‐targeting problem

By N. Baker, R. Daley. 2007

204 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/953d433616dbbe3fc388e374da22e06cfe194d69

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Citations in the corpus

Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, methodology): “Adjoint-DAS estimation of the observation sensitivity was considered in the work of Baker and Daley (2000) and Doerenbecher and Bergot (2001) as a tool to design observation targeting strategies.”
Significance of changes in medium-range forecast scores (2016, methodology): “Finally, innovative methods for diagnosing observation impact such as adjoint sensitivity (e.g. Baker and Daley, 2000) or ensemble spread (e.g. Harnisch et al., 2013) could be useful but because they are usually only applied to short-range verification, they face exactly the same issues.”
Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System (2008, methodology): “The reader is referred to Baker (2000) and Baker and Daley (2000) for more detailed descriptions of these dependencies.”
On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation (2008, methodology): “Diagnosis and tuning of error variances used in variational data assimilation are discussed in the work of Desroziers and Ivanov (2001) and Chapnik et al. (2006). A better understanding of how uncertainties in the specification of the error statistics will impact the analysis and forecast may be ach”
Data Assimilation for Numerical Weather Prediction: A Review (2009, background): “However exact statistical details of ME are difficult to obtain (Daley 1992a, b; Dee and Da Silva 1998; Zhu and Kamachi 2000) a fact which led researchers to suggest a variety of assumptions to approximate and parameterize the ME.”