Presentation on theme: "Critical issues of ensemble data assimilation in application to GOES-R risk reduction program D. Zupanski 1, M. Zupanski 1, M. DeMaria 2, and L. Grasso."— Presentation transcript:
Critical issues of ensemble data assimilation in application to GOES-R risk reduction program D. Zupanski 1, M. Zupanski 1, M. DeMaria 2, and L. Grasso 1 1 CIRA/Colorado State University, Fort Collins, CO 2 NOAA/NESDIS Fort Collins, CO Ninth Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface (IOAS-AOLS) 9-13 January 2005 San Diego, CA Dusanka Zupanski, CIRA/CSU Research partially supported by NOAA Grant NA17RJ1228
OUTLINE Critical data assimilation issues related to GOES-R satellite mission Ensemble based data assimilation methodology: Maximum Likelihood Ensemble Filter Experimental results Conclusions and future work Dusanka Zupanski, CIRA/CSU
Critical data assimilation issues of GOES-R and similar missions Assimilate satellite observations with high special and temporal resolution Employ state-of-the-art non-linear atmospheric models (without neglecting model errors) Provide optimal estimate of the atmospheric state Calculate uncertainty of the optimal estimate Determine amount of new information given by the observations Dusanka Zupanski, CIRA/CSU What is the value added of having new observations (e.g., GOES-R, CloudSat, GPM) ?
METHODOLOGY Maximum Likelihood Ensemble Filter (MLEF) (Zupanski 2005; Zupanski and Zupanski 2005) Developed using ideas from Variational data assimilation (3DVAR, 4DVAR) Iterated Kalman Filters Ensemble Transform Kalman Filter (ETKF, Bishop et al. 2001) MLEF is designed to provide optimal estimates of model state variables empirical parameters model error (bias) MLEF also calculates uncertainties of all estimates (in terms of P a and P f ) Dusanka Zupanski, CIRA/CSU
MLEF APPROACH - non-linear forecast model Minimize cost function J - model state vector of dim Nstate >>Nens Dusanka Zupanski, CIRA/CSU Analysis error covariance Forecast error covariance - information matrix of dim Nens Nens
EXPERIMENTAL DESIGN Hurricane Lili case 35 1-h DA cycles: 13UTC 1 Oct 2002 – 00 UTC 3 Oct CSU-RAMS non-hydrostatic model 30x20x21 grid points, 15 km grid distance (in the Gulf of Mexico) Control variable: u,v,w,theta,Exner, r_total (dim=54000) Model simulated observations with random noise (7200 obs per DA cycle) Nens=50 Iterative minimization of J (1 iteration only) Dusanka Zupanski, CIRA/CSU
Experimental design (continued) 13 UTC Cycle 1 14 UTC00 UTC Cycle 2Cycle 35 1 Oct Oct Oct UTC 2 Oct 2002 Cycle 33 Dusanka Zupanski, CIRA/CSU
Experimental design (continued) Dusanka Zupanski, CIRA/CSU 1200 w obs 1200 Exner obs 1200 theta obs Sub-cycles 1-4 Sub-cycles 5-8 Sub-cycles 9-12 Sub-cycles Sub-cycles Sub-cycles u obs 1200 v obs 1200 r_total obs Split cycle 33 into 24 sub-cycles Calculate eigenvalues of (I+C) -1/2 in each sub-cycle (information content) Information content of each group of observations
RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles 1-4 u- obs groups System is “learning” about the truth via updating analysis error covariance.
RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles 5-8 v- obs groups Most information in sub-cycles 5 and 6.
RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles 9-12 w- obs groups Most information in sub-cycle 10.
RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles Exner- obs groups
RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles theta- obs groups
RESULTS Dusanka Zupanski, CIRA/CSU Sub-cycles theta- obs groups Sub-cycles with little information can be excluded data selection.
CONCLUSIONS Dusanka Zupanski, CIRA/CSU Ensemble based data assimilation methods, such as the MLEF, can be effectively used to quantify impact of each observation type. The procedure is applicable to a forecast model of any complexity. Only eigenvalues of a small size matrix (Nens x Nens) need to be evaluated. Data assimilation system has a capability to learn form observations. Value added of having new observations (e.g., GOES-R, CloudSat, GPM) can be quantified applying a similar procedure.
References Zupanski, M., 2005: The Maximum Likelihood Ensemble Filter. Theoretical aspects. Accepted in Mon. Wea. Rev. [Available at ftp://ftp.cira.colostate.edu/milija/papers/MLEF_MWR.pdf] Zupanski, D., and M. Zupanski, 2005: Model error estimation employing ensemble data assimilation approach. Submitted to Mon. Wea. Rev. [Available at ftp://ftp.cira.colostate.edu/Zupanski/manuscripts/MLEF_model_err.revised2.pdf]