Diagnosis and adaptive tuning of observation‐error parameters in a variational assimilation

By G. Desroziers, S. Ivanov. 2001

215 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e76be58889e5a6d76952f2790f5d6edccd399c65

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Citations in the corpus

An extended specification of flow‐dependent background error variances in the Météo‐France global 4D‐Var system (2011, methodology): “8 is applied to standard deviations in , on the basis of a posteriori diagnostics (Desroziers and Ivanov, 2001), in order to represent model error contributions.”
A Description of the Advanced Research WRF Version 3 (2008, unknown): “These routines include both innovation vector-based approaches (Hollingsworth and Lonnberg, 1986) and variational tuning approaches (Desroziers and Ivanov, 2001).”
Accounting for correlated error in the assimilation of high‐resolution sounder data (2014, methodology): “If the observation errors used in the assimilation are correctly specified, then the final cost function value should be equal to the total number of observations divided by 2, as derived by Desroziers and Ivanov (2001) and Talagrand (1999).”
A Description of the Advanced Research WRF Model Version 4 (2019, methodology): “These routines include both innovation vector-based approaches (Hollingsworth and Lonnberg, 1986) and variational tuning approaches (Desroziers and Ivanov, 2001).”
Ensemble‐derived stationary and flow‐dependent background‐error covariances: Evaluation in a quasi‐operational NWP setting (2005, background): “…it is assumed that the covariances only depend on a sufficiently reduced set of parameters, then the approaches of Dee (1995), Talagrand (1999) and Desroziers and Ivanov (2001) may be useful for improving both the observation- and model-error covariances that will be necessary to obtain the…”
On the representation error in data assimilation (2018, unknown): “The interested reader may also like to explore other techniques, such as the maximum-likelihood method of Dee and da Silva (1999), the method based on analysis innovation statistics of Desroziers and Ivanov (2001), the online estimation methodofLi etal.”
Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter (2009, background): “Desroziers and Ivanov (2001) proposed an approach based on a consistency check of analysis to adaptively estimate either observation or both observation and background error statistics, assuming that the correlation lengths are very different for observation and background errors.”
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).”
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).”
An overview of the variational assimilation in the ALADIN/France numerical weather‐prediction system (2005, background): “The principle is to rely on the a posteriori validation of the total background-error variance, based on the following relationship (Desroziers and Ivanov 2001): R2 = Sb = E[J b min] Tr(HK) , where J bmin is the background cost function at the minimum, E[.] is the statistical expectation, H is the…”
Estimates of spatial and interchannel observation‐error characteristics for current sounder radiances for numerical weather prediction. I: Methods and application to ATOVS data (2010, methodology): “Desroziers and Ivanov (2001) proposed a method to tune scaling coefficients for the observationerror covariance based on an optimality criterion for the cost function at the minimum, assuming the correlations are accurately represented in the initially assumed observationerror covariance.”
Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system † (2014, methodology): “Desroziers and Ivanov (2001) used statistics from analysis innovations to tune background- and observationerror parameters, resulting in a successful description of the observation-error parameters in a 3D-Var framework.”
Error covariance estimation methods based on analysis residuals: theoretical foundation and convergence properties derived from simplified observation networks (2016, methodology): “In all estimation schemes proposed by Desroziers and colleagues (Desroziers and Ivanov, 2001; Chapnik et al., 2004; Desroziers et al., 2005), whether they use analysis residuals or a variational analysis scheme, the estimation methods are in essence static, in the sense that the analysis step (3)…”
A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results (2004, methodology): “A future paper will describe a more extensive application of this tuning together with a complementary ‘‘variational cost function diagnostic’’ tuning method (Desroziers and Ivanov 2001) using high-density surface observations.”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, methodology): “191 Following a distinct path, Desroziers and Ivanov (2001) proposed using the observation-minus192 analysis diagnostic.”
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…”