MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski

1. MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State UniversityFort Collins, CO 80523-1375 NCEP/EMC Sack Lunch Seminar Series 10 February 2004 ftp://ftp.cira.colostate.edu/Zupanski/presentations ftp://ftp.cira.colostate.edu/Zupanski/manuscripts

2. OUTLINE: • Data assimilation methods • State augmentation approach • Model error estimation employing variational and EnsDA frameworks • Experimental results employing various models • Conclusions and future work Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

3. DATA ASSIMILATION (ESTIMATION THEORY) Discrete stochastic-dynamic model wk-1 – model error (stochastic forcing) M – non-linear dynamic (NWP) model G – model (matrix) reflecting the state dependence of model error Discrete stochastic observation model ek – measurement + representativeness error H – non-linear observation operator (M  D ) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

4. DATA ASSIMILATION INCLUDES THE FOLLOWING: (1) State estimate (optimal solution): MAXIMUM LIKELIHOOD ESTIMATE (VARIATIONAL APPROACH ): MINIMUM VARIANCE ESTIMATE (KALMAN FILTER APPROACH ): (2) Estimate of the uncertainty of the solution: KALMAN FILTER APPROACH ENSEMBLE KALMAN FILTER or EnsDA APPROACH In EnsDA solution is defined in ensemble subspace (reduced rank problem) !

5. State augmentation approach (a model bias example) Derber MWR, 1989 Control variable for the analysis cycle k: Solve EnKF equations (or EnsDA) equations in terms of control variable z and forecast model F : Parameter estimation is a special case of state augmentation approach! Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

6. 4DVAR framework Forecast error covariance Observations First guess Data assimilation (Init. Cond. and Model Error adjust.) Init. Cond. and Model Error opt. estimates EnsDA framework Forecast error covariance Observations First guess Data assimilation (Init. Cond. and Model Error adjust.) Analysis error Covariance (in ensemble subspace) Init. Cond. and Model Error opt. estimates Ens. forecasting In EnsDA framework model error does not depend on assumptions regarding forecast error covariance; data assimilation problem is solved in ensemble subspace

7. ETA 4DVAR: Surface pressuremodel error time evolution (every 2-h over a 12-h data assimilation interval) From Zupanski et al. 2004 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

8. RAMS 4DVAR: Exner functionmodel error time evolution (lev=5km), every 2-h From Zupanski et al. 2004 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

9. Eta: Horizontal windmodel error time evolution (lev=250hPa), every 2-h Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

10. RAMS: Horizontal windmodel error time evolution (lev=250hPa), every 2-h Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

11. RAMS: Horizontal windmodel error, vertical cross-section, every 2-h Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

12. EnsDA experiments with Korteweg-de Vries-Burgers (KdVB) model - one-dimensional model - includes non-linear advection, diffusion and dispersion PARAMETER estimation impact From Zupanski and Zupanski 2004 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

13. EnsDA experiments with KdVB model Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

14. Kalman Filter statistical verification toolsInnovation statistics Innovation vector =obs – first guess • c2 statistics – can be used to test the stability of an ensemble filter Pf – produced by Ensemble Filter algorithm R – input to Ensemble Filter algorithm The conditional mean of c2 (normalized by obs) should be equal to one • Innovation histogram – Probability Density Function of normalized innovation vectors For Gaussian distribution, and with linear observation operator H, the innovation histogram should be equal to standard normal distribution N (0,1)

15. EnsDA experiments with KdVB model (PARAMETER estimation impact) 10 obs 101 obs

16. EnsDA experiments with KdVB model (PARAMETER estimation impact) It would be BEST to have a perfect model, but since this is not the case, it is necessary to estimate model error and use it to correct the model!

17. EnsDA experiments with KdVB model BIAS estimation results BIAS estimation may require many observations and large ensemble size ! A feasible solution to reduce the number of degrees of freedom is to define bias in terms of small number of parameters.

18. EnsDA experiments with KdVB model Analysis error covariance matrix (UNCERTAINTY estimate) From Zupanski and Zupanski 2004 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

19. We are employing more and more models! NASA’s GEOS column model • Work in progress in collaboration with: • A. Hou and S. Zhang (NASA/GMAO) • C. Kummerow (CSU/Atmos. Sci.) Preliminary results including parameter estimation: R1/2 = e R1/2 = 2e Choice of observation errors directly impacts innovation statistics. Observation error covariance R is the only given input to the system!

20. Finally, we want to do the real thing employing EnsDA! RAMS and WRF models • Work has just started in collaboration with: • M. Zupanski, M. DeMaria and L. Grasso (CSU/CIRA) NOAA/NESDIS GOES-R Risk Reduction Project Lead by M. DeMaria • Use the EnsDA framework with RAMS and/or WRF to assess GOES-R sounder capabilities using simulated data (identical twin experiments) • Use a similar framework to assimilate AIRS soundings for mesoscale case studies (severe weather, tropical cyclone, lake effect snow) • Continue the mesoscale studies when CrIS soundings become available from the NPP mission. Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

21. CONCLUSIONS • To employ full data assimilation power, model error estimation should be included • Model error estimation is an important diagnostic and model development tool (estimate and correct model errors during the model development phase) • EnsDA approaches are very promising since they can provide not only optimal estimate of the atmospheric state, but the uncertainty of the estimate as well FUTURE WORK • Estimate and correct model errors for various models (GEOS, RAMS, WRF, etc.) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

22. Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

23. An example: Equivalence between variational and Kalman filter equations (for linear models and Gaussian statistics)

24. An example (continued): Using the matrix equality (e. g., Jaswinski 1970, Appendix 7b: We obtain the Kalman filter analysis equation: Important difference: variational methods DO NOT provide forecast error covariance update (update of P)! Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu