Empirical determination of the covariance of forecast errors: An empirical justification and reformulation of hybrid covariance models

[1]  M. Buehner,et al.  Atmospheric Data Assimilation with an Ensemble Kalman Filter: Results with Real Observations , 2005 .

[2]  Shunji Kotsuki,et al.  Weight structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model , 2020, Quarterly Journal of the Royal Meteorological Society.

[3]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[4]  E. Kalnay,et al.  Balance and Ensemble Kalman Filter Localization Techniques , 2011 .

[5]  J. Wishart THE GENERALISED PRODUCT MOMENT DISTRIBUTION IN SAMPLES FROM A NORMAL MULTIVARIATE POPULATION , 1928 .

[6]  Anthony Hollingsworth,et al.  The statistical structure of short-range forecast errors as determined from radiosonde data , 1986 .

[7]  T. Auligne,et al.  Optimized Localization and Hybridization to Filter Ensemble-Based Covariances , 2015 .

[8]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[9]  P. Bickel,et al.  Obstacles to High-Dimensional Particle Filtering , 2008 .

[10]  T. Hamill,et al.  A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme , 2000 .

[11]  Xuguang Wang,et al.  GSI-Based Four-Dimensional Ensemble–Variational (4DEnsVar) Data Assimilation: Formulation and Single-Resolution Experiments with Real Data for NCEP Global Forecast System , 2014 .

[12]  Massimo Bonavita,et al.  The ensemble Kalman filter in an operational regional NWP system: preliminary results with real observations , 2008 .

[13]  Jonathan Flowerdew Towards a theory of optimal localisation , 2015 .

[14]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[15]  A. Lorenc,et al.  Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office , 2013 .

[16]  Craig H. Bishop,et al.  Hidden Error Variance Theory. Part I: Exposition and Analytic Model , 2013 .

[17]  F. Molteni Atmospheric simulations using a GCM with simplified physical parametrizations. I: model climatology and variability in multi-decadal experiments , 2003 .

[18]  P. Houtekamer,et al.  A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation , 2001 .

[19]  Roland Potthast,et al.  Particle filters for applications in geosciences , 2018, 1807.10434.

[20]  P. Leeuwen,et al.  Nonlinear data assimilation in geosciences: an extremely efficient particle filter , 2010 .

[21]  Colin J. Cotter,et al.  Probabilistic Forecasting and Bayesian Data Assimilation , 2015 .

[22]  A. Hollingsworth,et al.  The statistical structure of short-range forecast errors as determined from radiosonde data Part II: The covariance of height and wind errors , 1986 .

[23]  J. Whitaker,et al.  Ensemble Data Assimilation without Perturbed Observations , 2002 .

[24]  P. Houtekamer,et al.  Data Assimilation Using an Ensemble Kalman Filter Technique , 1998 .

[25]  Jeffrey L. Anderson An Ensemble Adjustment Kalman Filter for Data Assimilation , 2001 .

[26]  T. Miyoshi The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter , 2011 .

[27]  Craig H. Bishop,et al.  Comparison of Hybrid Ensemble/4DVar and 4DVar within the NAVDAS-AR Data Assimilation Framework , 2013 .