Diagnosis and tuning of observational error in a quasi‐operational data assimilation setting

By Bernard Chapnik, G. Desroziers, Florence Rabier, O. Talagrand. 2006

127 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/962d64dfd2eb0ab924eedf5d09be20ca74297c06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

The quiet revolution of numerical weather prediction (2015, methodology): “…with computationally efficient radiative transfer models 41,42 , the much refined characterization of short-range forecast 43 and observation errors 44 using state dependent weights for each, and better use of observations arising from significant improvements of physical parameterizations 45 .”
Data Assimilation for Numerical Weather Prediction: A Review (2009, methodology): “Chapnik et al. (2006) use evaluation of trace of the KH matrix, using a method put forward by Desroziers and Ivanov (2001) to evaluate trace of KH. Computing sensitivity of forecast to the observations can be carried out by considering the adjoint of data assimilation together with the adjoint of…”
Assimilating Along-Track Altimetric Observations through Local Hydrostatic Adjustment in a Global Ocean Variational Assimilation System (2011, methodology): “The procedure to compute DFS is based on a randomization technique firstly explored by Chapnik et al. (2006), to whom the reader is referred for further details.”
Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation (2010, methodology): “This is equivalent to the geometrical interpretation of the analysis in terms of projection using the Mahalanobis norm as a scalar product (Desroziers et al. 2005; Chapnik et al. 2006).”
Error covariance estimation methods based on analysis residuals: theoretical foundation and convergence properties derived from simplified observation networks (2016, methodology): “The variational approach has been used extensively in meteorological applications (e.g. Chapnik et al., 2004, 2006; Brousseau et al., 2014) because of its extension to observation subsets by the use of a projection operator and a formalism developed by Talagrand (1999, 2010).”
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, methodology): “al., 2004; Chapnik et al., 2006 ) suggests that the innovation (observations minus background)”
On the representation error in data assimilation (2018, unknown): “…relating to the diagnostic under some simplifying assumptions provide information on how to interpret the results of iterating the diagnostic when the errors used in the assimilation are not exact (Chapnik et al. , 2004, 2006; Desroziers et al. , 2005, 2009; M´enard et al. , 2009; M´enard, 2016).”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, methodology): “A particular case of practical significance is given by the parametric specification B(sb) = sbB, Ri(soi ) = soi Ri, i ∈ I, (38) which is a common representation used to perform error covariance tuning (Desroziers and Ivanov, 2001; Chapnik et al., 2006; Desroziers et al., 2009).”
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).”