Parameter Estimation Using Ensemble-Based Data Assimilation in the Presence of Model Error

AbstractThis work explores the potential of online parameter estimation as a technique for model error treatment under an imperfect model scenario, in an ensemble-based data assimilation system, using a simple atmospheric general circulation model, and an observing system simulation experiment (OSSE) approach. Model error is introduced in the imperfect model scenario by changing the value of the parameters associated with different schemes. The parameters of the moist convection scheme are the only ones to be estimated in the data assimilation system. In this work, parameter estimation is compared and combined with techniques that account for the lack of ensemble spread and for the systematic model error. The OSSEs show that when parameter estimation is combined with model error treatment techniques, multiplicative and additive inflation or a bias correction technique, parameter estimation produces a further improvement of analysis quality and medium-range forecast skill with respect to the OSSEs with mod...

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

[2]  Takemasa Miyoshi,et al.  Estimating Model Parameters with Ensemble-Based Data Assimilation: Parameter Covariance Treatment , 2013 .

[3]  Xinrong Wu,et al.  Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model , 2012 .

[4]  T. Delworth,et al.  A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model , 2012 .

[5]  M. Pulido,et al.  Gravity‐wave drag estimation from global analyses using variational data assimilation principles. II: Case study , 2006 .

[6]  E. Kalnay,et al.  Ensemble Kalman filter data assimilation of Thermal Emission Spectrometer temperature retrievals into a Mars GCM , 2012 .

[7]  E. Kalnay,et al.  Improving EnKF spin-up for typhoon assimilation and prediction , 2011 .

[8]  Robert Pincus,et al.  Parameter estimation using data assimilation in an atmospheric general circulation model: From a perfect toward the real world , 2013 .

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

[10]  Jeffrey L. Anderson.  Spatially and temporally varying adaptive covariance inflation for ensemble filters , 2009 .

[11]  Thomas M. Hamill,et al.  Ensemble Data Assimilation with the NCEP Global Forecast System , 2008 .

[12]  Takemasa Miyoshi,et al.  Estimating Model Parameters with Ensemble-Based Data Assimilation: A Review , 2013 .

[13]  Edward Ott,et al.  State and parameter estimation of spatiotemporally chaotic systems illustrated by an application to Rayleigh-Bénard convection. , 2009, Chaos.

[14]  Christopher M. Danforth,et al.  Accounting for Model Errors in Ensemble Data Assimilation , 2009 .

[15]  Guifu Zhang,et al.  Simultaneous Estimation of Microphysical Parameters and the Atmospheric State Using Simulated Polarimetric Radar Data and an Ensemble Kalman Filter in the Presence of an Observation Operator Error , 2010 .

[16]  Kayo Ide,et al.  “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation , 2011 .

[17]  C. Jakob.  Accelerating progress in global atmospheric model development through improved parameterizations: challenges, opportunities, and strategies , 2010 .

[18]  Istvan Szunyogh,et al.  Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter , 2005, physics/0511236.

[19]  T. Palmer,et al.  Stochastic representation of model uncertainties in the ECMWF ensemble prediction system , 2007 .

[20]  Fuqing Zhang,et al.  Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments , 2006 .

[21]  Hiroshi Koyama,et al.  Reducing Forecast Errors Due to Model Imperfections Using Ensemble , 2010 .

[22]  Istvan Szunyogh,et al.  Assimilating non-local observations with a local ensemble Kalman filter , 2007 .

[23]  Takemasa Miyoshi,et al.  ENSEMBLE KALMAN FILTER EXPERIMENTS WITH A PRIMITIVE-EQUATION GLOBAL MODEL , 2005 .

[24]  Jesse Berwald,et al.  Nonglobal Parameter Estimation Using Local Ensemble Kalman Filtering , 2013, 1306.3488.

[25]  Arlindo da Silva,et al.  Data assimilation in the presence of forecast bias , 1998 .

[26]  B. Martin.  PARAMETER ESTIMATION , 2012, Statistical Methods for Biomedical Research.

[27]  James D. Annan,et al.  Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter , 2005 .

[28]  Leonard A. Smith,et al.  Uncertainty in predictions of the climate response to rising levels of greenhouse gases , 2005, Nature.

[29]  Takemasa Miyoshi,et al.  Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution , 2007 .

[30]  Istvan Szunyogh,et al.  Local ensemble Kalman filtering in the presence of model bias , 2006 .

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

[32]  Takemasa Miyoshi,et al.  Accelerating the EnKF Spinup for Typhoon Assimilation and Prediction , 2012 .

[33]  Fuqing Zhang,et al.  Ensemble-based simultaneous state and parameter estimation in a two-dimensional sea-breeze model , 2006 .

[34]  A. Aksoy.  NUMERICAL MODELS | Parameter Estimation , 2015 .

[35]  Fuqing Zhang,et al.  Ensemble‐based simultaneous state and parameter estimation with MM5 , 2006 .

[36]  Mingjing Tong,et al.  Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments , 2008 .

[37]  J. Whitaker,et al.  Evaluating Methods to Account for System Errors in Ensemble Data Assimilation , 2012 .

[38]  James D. Annan,et al.  Parameter estimation using chaotic time series , 2005 .

[39]  J. Whitaker,et al.  Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter , 2001 .

[40]  X. Deng,et al.  Model Error Representation in an Operational Ensemble Kalman Filter , 2009 .

[41]  Michael Ghil,et al.  Data Assimilation for a Coupled Ocean–Atmosphere Model. Part II: Parameter Estimation , 2008 .

[42]  C. Danforth,et al.  Estimating and Correcting Global Weather Model Error , 2007 .

[43]  G. Shutts.  A kinetic energy backscatter algorithm for use in ensemble prediction systems , 2005 .

[44]  Jeffrey L. Anderson.  Spatially and temporally varying adaptive covariance inflation for ensemble filters , 2009 .

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

[46]  Takemasa Miyoshi,et al.  Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF) , 2007 .