Diagnosis of observation, background and analysis‐error statistics in observation space

By G. Desroziers, L. Berre, Bernard Chapnik, Paul Poli. 2005

885 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c178c570e8d9044d25b59fa8488f4f6129bc05c4

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Citations in the corpus

The benefits of correlated observation errors for small scales (2015, methodology): “Desroziers et al. (2005) describe a technique that estimates all elements of the observation-error covariance matrix; a sufficient condition for these estimates to be accurate is that the data assimilation scheme is optimal in the sense of providing the true minimum error variance estimate.”
The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter (2011, methodology): “Alternatively, LKM09 used the innovation statistics of Desroziers et al. (2005) and applied a Kalman filter analysis update to the inflation parameters based on the Gaussian assumption.”
Reanalyses Suitable for Characterizing Long-Term Trends (2010, methodology): “Generally, they also make assumptions regarding the distribution of observational errors, which are not strictly valid in the real world (Desroziers et al. 2005).”
Accounting for correlated error in the assimilation of high‐resolution sounder data (2014, methodology): “Section 3 shows the results of performing a diagnostic technique described by Desroziers et al. (2005) on IASI data to estimate the true structure of the observation-error covariance matrices.”
Enhanced Adaptive Inflation Algorithm for Ensemble Filters (2018, methodology): “From the definition of the innovation (Desroziers et al. 2005), the following relations can be derived: d5 y2 x b 5 « o 1 (x t 2 x b )5 « o 1 « b , (4) E(d)5E(« o )1 E(« b )5 0, (5) E(d2)5 E(«2o)1E(« 2 b)1 2E(«o«b) , (6) 5s2o 1s 2 b , (7) where «b is the background error following N(0, s 2 b), and…”
Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model (2014, methodology): “Different algorithms have been proposed to estimate a posteriori observation error statistics (variances and spatial correlations) by Hollingsworth and L€onnberg (1986), Desroziers et al. (2005), and Fisher (2003).”
Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere (2017, methodology): “2002 S. Kotsuki et al. Several studies have proposed adaptive covariance inflation methods to avoid the expensive tuning and to estimate the inflation parameters adaptively based on the observation data or observation-space statistics (Desroziers et al., 2005).”
The error of representation: basic understanding (2015, positive): “Lastly, it was shown that innovation-based techniques (e.g. Hollingsworth and Lo¨nnberg, 1986; Desroziers et al., 2006) for the estimation of representation error covariance matrices are self-consistent estimators of the representation error only when the observation operator has been modified to…”
On the representation error in data assimilation (2018, positive): “…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).”
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): “For the current article, we do not use the diagnostic observation-error characteristics in subsequent assimilations, i.e. we show results only after one iteration of the tuning method suggested by Desroziers et al. (2005).”
Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters (2010, methodology): “In the context of statistical linear estimation, information extracted from the innovations y − Hxb and residuals y − Hxa is used to derive consistency diagnostics of observation- and background-error statistics in the DAS (Talagrand, 1999; Daley and Barker, 2001; Desroziers et al., 2005).”
The impact of Aeolus wind retrievals on ECMWF global weather forecasts (2021, methodology): “Estimates for 𝜎(𝜖B) were derived using “Desroziers diagnostics” (Desroziers et al., 2005) using radiosonde zonal wind and Aeolus HLOS wind departures.”
Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system † (2014, positive): “This approach has been used to estimate error variances and interchannel correlations in both the Météo-France (Desroziers et al., 2005) and ECMWF (Bormann et al.”
A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation (2018, methodology): “Then, Dreano et al. (2017) and Pulido et al.”
A Second-Order Exact Ensemble Square Root Filter for Nonlinear Data Assimilation (2015, background): “Specifically, the expected innovation variance (Desroziers et al. 2005) can be expressed as the sum of the observation error variance and average prior ensemble variance in observation space, (s exp inno) 2 5 trace[Y0f (Y 0 f ) T/(m2 1)]/k1s2obs.”
Data assimilation: making sense of Earth Observation (2014, methodology): “Desroziers et al. (2005) provides a method to evaluate observational, model, and analyses errors.”
“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation (2011, methodology): “Li et al. [2009] estimated simultaneously the adaptive inflation and observation errors, using the equations derived by Desroziers et al. [2005] .”
An extended specification of flow‐dependent background error variances in the Météo‐France global 4D‐Var system (2011, methodology): “Therefore, it is also planned to work on inflation techniques within the ensemble simulation, by using a posteriori diagnostics (Desroziers et al., 2005, 2009; Miyoshi, 2005).”