Exploring the potential and limitations of weak‐constraint 4D‐Var

[1]  Milija Zupanski,et al.  Regional Four-Dimensional Variational Data Assimilation in a Quasi-Operational Forecasting Environment , 1993 .

[2]  Lance M. Leslie,et al.  A Two-Layer Quasi-Geostrophic Model of Summer Trough Formation in the Australian Subtropical Easterlies , 1984 .

[3]  Y. Trémolet.  Accounting for an imperfect model in 4D‐Var , 2006 .

[4]  M. Bocquet,et al.  An iterative ensemble Kalman filter in presence of additive model error November 10 , 2017 , 2018 .

[5]  Ricardo Todling,et al.  A lag‐1 smoother approach to system‐error estimation: sequential method , 2015 .

[6]  Paul Poli,et al.  CERA‐20C: A Coupled Reanalysis of the Twentieth Century , 2018 .

[7]  Paul Poli,et al.  Diagnosis of observation, background and analysis‐error statistics in observation space , 2005 .

[8]  Chris Snyder,et al.  Linear Evolution of Error Covariances in a Quasigeostrophic Model , 2003 .

[9]  D. Cariolle,et al.  Accounting for model error in air quality forecasts: an application of 4DEnVar to the assimilation of atmospheric composition using QG-Chem 1.0 , 2016 .

[10]  Y. Trémolet.  Model‐error estimation in 4D‐Var , 2007 .

[11]  A. Hollingsworth,et al.  Some aspects of the improvement in skill of numerical weather prediction , 2002 .

[12]  Jeffrey Humpherys,et al.  A Fresh Look at the Kalman Filter , 2012, SIAM Rev..

[13]  J. Derber.  A Variational Continuous Assimilation Technique , 1989 .

[14]  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 .

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

[16]  D. Dee,et al.  Variational bias correction of satellite radiance data in the ERA‐Interim reanalysis , 2009 .

[17]  Massimo Bonavita,et al.  The evolution of the ECMWF hybrid data assimilation system , 2016 .

[18]  Y. Sasaki.  SOME BASIC FORMALISMS IN NUMERICAL VARIATIONAL ANALYSIS , 1970 .

[19]  Marc Bocquet,et al.  An Iterative Ensemble Kalman Smoother in Presence of Additive Model Error , 2020, SIAM/ASA J. Uncertain. Quantification.

[20]  D. P. DEE,et al.  Bias and data assimilation , 2005 .

[21]  Ricardo Todling.  A Complementary Note to 'A Lag-1 Smoother Approach to System-Error Estimation': The Intrinsic Limitations of Residual Diagnostics , 2015 .

[22]  Peter Bauer,et al.  GNSS Radio Occultation Constellation Observing System Experiments , 2014 .

[23]  J. R. Eyre,et al.  Observation bias correction schemes in data assimilation systems: a theoretical study of some of their properties , 2016 .

[24]  Selime Gürol,et al.  Parallelization in the time dimension of four‐dimensional variational data assimilation , 2017 .

[25]  Leonard A. Smith,et al.  Linear Regime Duration: Is 24 Hours a Long Time in Synoptic Weather Forecasting? , 2001 .

[26]  Dick Dee,et al.  Adaptive bias correction for satellite data in a numerical weather prediction system , 2007 .

[27]  L. Leslie,et al.  Generalized inversion of a global numerical weather prediction model , 1996 .

[28]  S. Healy,et al.  Towards an unbiased stratospheric analysis , 2020, Quarterly Journal of the Royal Meteorological Society.