Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate

1. CHALLENGING SCIENTIFIC ISSUES OF NAME IN THE CONTEXT OF DATA ASSIMILATION Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State UniversityFort Collins, CO 80523-1375 NAME meeting June 6 2003 Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

2. CHALLENGES OF DATA ASSIMILATION AND NAME: • USING ALL AVAILABLE OBSERVATIONS • (routine + NAME) • OPTIMAL ESTIMATE OF THE ATMOSPHERIC STATE • (optimal estimate of monsoon features) • MODEL ERROR ESTIMATION • (impact of model error on monsoon simulation) • UNCERTAINTY INFORMATION • (how much we can trust the simulated monsoon features?) • FORECAST PROBABILITY • (sources and limits of monsoon predictability) • UNIFIED METHODOLOGY • (can all of the above be addressed by a single method?) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

3. WHAT IS IMPORTANT? • Time dimension (4-dimensional methods suitable) • Non-linear interactions (non-linear methods) • Realistic models Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

4. OPTIMAL ESTIMATE OF THE ATMOSPHERIC STATE • NAME OBSERVATIONS: • Raingauge (event logging) precipitation • Doppler radar precipitation • UHF wind profiler • Soil moisture sensors • Other • WHAT ARE SUITABLE DATA ASSIMILATION METHODS ? • Variational (ECMWF, NCEP, Meteo France, UK Met Office) • Nudging (NCEP) • Kalman filter/smoother (NASA) • Ensemble data assimilation Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

5. NEW METHODOLOGY Ensemble data assimilation (EnsDA) (e. g., Evensen 1994; Bishop et al. 2001; Anderson 2001) • BASED ON KALMAN FILTER THEORY • NON-LINEAR ENSEMBLE FORECASTING USED TO CALCULATE ERROR COVARIANCES • NO ADJOINT MODELS USED • EASY APPLICATIONS TO ANY MODEL, OBSERVATIONS Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

6. 2A12 obs 1D-Var/RR 2A12 1D-Var/BT PATER obs 1D-Var/RR PATER ECMWF 1D-Var results Background Case of super-typhoon MITAG (5 March 2002 @1200 UTC) TMI data Surface rainfall rates (mm hr-1) (Moreau et al. 2003) From Martin Miller (ECMWF)

7. CIRA/CSU 4DVAR: From Vukicevic et al. 2003 (submitted to MWR)

8. MODEL ERROR ESTIMATION • Estimate: • Serially correlated model error (bias) • Horizontal boundary conditions error • Parameter error • WHAT ARE SUITABLE METHODS ? • Variational (e. g., Derber 1989; Zupanski 1997) • Nudging (e. g., Kaas et al. 1999) • Kalman filter/smoother (Dee 1995; Dee and da Silva 1998) • Ensemble data assimilation (Anderson 2001) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

9. NCEP’s ETA model 4DVAR: TIME EVOLUTION OF OPTIMIZED MODEL ERROR Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

10. NCEP’s ETA model 4DVAR: TIME EVOLUTION OF OPTIMIZED MODEL ERROR Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

11. NCEP’s ETA model 4DVAR: TIME EVOLUTION OF OPTIMIZED MODEL ERROR MODEL ERROR IS MOVING FAST Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

12. NASA’s PSAS: Station locations, 300-hPa moisture forecast, bias estimate, and final moisture analysis near Indonesia for 0000 UTC 1 Jan 1998. Contour values range from 0 g kg 1 (lightest) to 0.8 g kg 1 (darkest) by steps of 0.1 g kg 1. From Dee and Todling 2000 (MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

13. EnsDA experiments with Korteweg-de Vries-Burgers (KdVB) model - one-dimensional model - includes non-linear advection, diffusion and dispersion From Zupanski and Zupanski 2003 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

14. UNCERTAINTY INFORMATION • Uncertainty information defined by the error covariance matrix in terms of: • Initial conditions (IC) covariance • Model error (ME) covariance • Cross covariance (IC-ME) • WHAT ARE SUITABLE METHODS ? • Kalman filter/smoother • Ensemble data assimilation Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

15. EnsDA experiments with KdVB model Analysis error covariance matrix From Zupanski and Zupanski 2003 (submitted to MWR) Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

16. FORECAST PROBABILITY • Ensemble forecasting , starting from perturbations defined by: • Initial conditions (IC) covariance • Model error (ME) covariance • Cross covariance (IC-ME) • For realistic models • WHAT ARE SUITABLE METHODS ? • Ensemble data assimilation + ensemble forecasting Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

17. CAN WE DEFINE UNIFIED METHODOLOGY SATISFYING THE COMPLETE WISH LIST: • USING ALL AVAILABLE OBSERVATIONS • OPTIMAL ESTIMATE OF THE ATMOSPHERIC STATE • MODEL ERROR ESTIMATION • UNCERTAINTY INFORMATION • FORECAST PROBABILITY • Ensemble data assimilation + ensemble forecasting • is most PROMISING • This methodology NEVER TESTED under complex and realistic conditions such as NAME CHALLENGE AND OPPORTUNITY OF NAME Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu

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

19. Model background T,qv 1D-Var analysis increments of T and qv Jacobians of convection & cloud scheme (Marécal & Mahfouf, 2002) Convection + Cloud scheme (PL, AT & MJ) TMI or SSMI retrieved rainfall rates simulated 3D rain and ql simulated surface rain Radiative Transfer Model (PBa) Adjoint of the RTM (EM) TMI or SSMI MW BTs (10,19,22,37 GHz) simulated MW Tb 1D-Var assimilation of TMI/SSMI brightness temperatures From Martin Miller (ECMWF)

20. 3h forecast after assimilation Key results New forecast slightly better where cloud cover is correct (-0.5 vs 0.1 K Tb error) Neither forecast captures fast dissipation in south-west Texas due to the LBC error From Vukicevic et al. 2003 (submitted to MWR)

21. ETA model 4DVAR experiment: OPTIMAL IC OPTIMAL MODEL ERROR DIFFERENCE: 2-3 ORDERS OF MAGNITUDE Dusanka Zupanski, CIRA/CSU Zupanski@CIRA.colostate.edu