Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters
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[1] Shian‐Jiann Lin. A “Vertically Lagrangian” Finite-Volume Dynamical Core for Global Models , 2004 .
[2] Dick Dee,et al. Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology , 1999 .
[3] Dacian N. Daescu,et al. NOTES AND CORRESPONDENCE On the Deterministic Observation Impact Guidance: A Geometrical Perspective , 2009 .
[4] Stephen E. Cohn,et al. An Introduction to Estimation Theory (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .
[5] Dick Dee,et al. Ooce Note Series on Global Modeling and Data Assimilation Maximum-likelihood Estimation of Forecast and Observation Error Covariance Parameters , 2022 .
[6] Robert Atlas,et al. Effects of data selection and error specification on the assimilation of AIRS data , 2007 .
[7] Y. Trémolet. Incremental 4D-Var convergence study , 2007 .
[8] Ross N. Bannister,et al. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances , 2008 .
[10] Y. Trémolet. First-order and higher-order approximations of observation impact , 2007 .
[11] R. Todling,et al. Adjoint Estimation of the Variation in Model Functional Output due to the Assimilation of Data , 2009 .
[12] R. Errico,et al. Examination of various-order adjoint-based approximations of observation impact , 2007 .
[13] Jacques Verron,et al. Sensitivity Analysis in Variational Data Assimilation (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .
[14] Christopher K. Wikle,et al. Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.
[15] J. Magnus,et al. Matrix Differential Calculus with Applications in Statistics and Econometrics , 2019, Wiley Series in Probability and Statistics.
[16] Roger Daley,et al. NAVDAS: Formulation and Diagnostics , 2001 .
[17] Roger Daley,et al. The application of Kalman smoother theory to the estimation of 4DVAR error statistics , 1996 .
[18] Gérald Desroziers,et al. Diagnosis and adaptive tuning of observation‐error parameters in a variational assimilation , 2001 .
[19] Rolf H. Langland,et al. Diagnostics for Evaluating the Impact of Satellite Observations , 2009 .
[20] P. Courtier,et al. A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .
[21] Jacques Verron,et al. Sensitivity Analysis in Variational Data Assimilation , 1997 .
[22] Andrew C. Lorenc,et al. Modelling of error covariances by 4D‐Var data assimilation , 2003 .
[23] Rolf H. Langland,et al. Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system , 2004 .
[24] J. Magnus,et al. Matrix Differential Calculus with Applications in Statistics and Econometrics , 1991 .
[25] Rolf H. Langland,et al. Observation Impact during the North Atlantic TReC—2003 , 2005 .
[26] O. Talagrand,et al. Diagnosis and tuning of observational error in a quasi‐operational data assimilation setting , 2006 .
[27] S. Cohn,et al. Ooce Note Series on Global Modeling and Data Assimilation Construction of Correlation Functions in Two and Three Dimensions and Convolution Covariance Functions , 2022 .
[28] E. Kalnay,et al. Estimating observation impact without adjoint model in an ensemble Kalman filter , 2008 .
[29] E. Kalnay,et al. Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter , 2009 .
[30] Yannick Trémolet. Incremental 4D-Var convergence study , 2007 .
[31] D. Cacuci,et al. SENSITIVITY and UNCERTAINTY ANALYSIS , 2003 .
[32] R. Purser,et al. Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances , 2002 .
[33] Moustafa T. Chahine,et al. Improving Global Analysis and Forecasting with AIRS , 2006 .
[34] R. Gelaro,et al. Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System , 2008 .
[35] D. Daescu. On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation , 2008 .
[36] R. Bannister. A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics , 2008 .
[37] Ronald Gelaro,et al. Examination of observation impacts derived from observing system experiments (OSEs) and adjoint models , 2009 .
[38] S. Cohn,et al. An Introduction to Estimation Theory , 1997 .
[39] Roger Daley,et al. Observation and background adjoint sensitivity in the adaptive observation‐targeting problem , 2007 .
[40] K. Emanuel,et al. Optimal Sites for Supplementary Weather Observations: Simulation with a Small Model , 1998 .
[41] Mark Buehner,et al. Evaluation of new estimates of background- and observation-error covariances for variational assimilation , 2005 .
[42] T. Bergot,et al. Sensitivity to observations applied to FASTEX cases , 2001 .
[43] Thomas Kaminski,et al. Tangent Linear and Adjoint Versions of NASA/GMAO’s Fortran 90 Global Weather Forecast Model , 2006 .
[44] R. Daley. Atmospheric Data Analysis , 1991 .
[45] C. Cardinali. Monitoring the observation impact on the short‐range forecast , 2009 .
[46] Yannick Trémolet,et al. Computation of observation sensitivity and observation impact in incremental variational data assimilation , 2008 .
[47] Rod Frehlich,et al. Adaptive data assimilation including the effect of spatial variations in observation error , 2006 .
[48] T. Hamill,et al. Using Improved Background-Error Covariances from an Ensemble Kalman Filter for Adaptive Observations , 2002 .
[49] Ryan D. Torn,et al. Ensemble-Based Sensitivity Analysis , 2008 .
[50] D. Dee. On-line Estimation of Error Covariance Parameters for Atmospheric Data Assimilation , 1995 .
[51] A. Joly,et al. Adjoint sensitivity of the forecast to TOVS observations , 2002 .
[52] Ricardo Todling,et al. The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0 , 2008 .
[53] Florence Rabier,et al. Properties and first application of an error‐statistics tuning method in variational assimilation , 2004 .
[54] Paul Poli,et al. Diagnosis of observation, background and analysis‐error statistics in observation space , 2005 .
[55] L. Berre,et al. A Posteriori Diagnostics in an Ensemble of Perturbed Analyses , 2009 .
[56] Thierry Bergot,et al. A study on the optimization of the deployment of targeted observations using adjoint‐based methods , 2002 .
[57] A. Jazwinski. Stochastic Processes and Filtering Theory , 1970 .
[58] O. Talagrand. Objective Validation and Evaluation of Data Assimilation , 2003 .
[59] Liang Xu,et al. Development of NAVDAS-AR: non-linear formulation and outer loop tests , 2006 .
[60] Stephen E. Cohn,et al. Treatment of Observation Error due to Unresolved Scales in Atmospheric Data Assimilation , 2006 .