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P2.1 ENSEMBLE DATA ASSIMILATION: EXPERIMENTS USING NASA’S GEOS COLUMN PRECIPITATION MODEL D. Zupanski 1, A. Y. Hou 2, S. Zhang 2, M. Zupanski 1, C. D. Kummerow 1, and S. H. Cheung 3 1 Colorado State University, Fort Collins, CO 2 NASA Goddard Space Flight Center, Greenbelt, MD 3 NASA Ames Research Center, Moffett Field, CA Goals Develop a unified probabilistic data assimilation, model error estimation, and ensemble forecasting method Examine the method in application to NASA’s GEOS column models Assimilate satellite precipitation observations (SSM/I, TMI, GPM) Estimate atmospheric state, model errors, and model’s empirical parameters Determine uncertainty of all estimates Evaluate capability of the method to provide new knowledge about atmospheric processes 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) Initial tuning of the MLEF algorithm in application to NASA/GEOS-4 column model Single column version of the GOES-4 GCM 55 level model, two state variables: T and Q 10 ensembles, 110 observations of T and Q 10 data assimilation cycles 6-h data assimilation interval PSAS analyses used as “observations” and forcing Minimize cost function J Change of variable - augmented control variable of dim Nstate >>Nens (includes initial conditions, model error, empirical parameters) - control variable in ensemble space of dim Nens Analysis error covariance Forecast error covariance Observability matrix Degrees of freedom (DOF) for signal (Rodgers 2000) - eigenvalues of C Experiments Examine sensitivity of innovation statistics to observation errors Evaluate information content of the observations (DOF) Further experiments employing NASA/GEOS-5 column model Single column version of the GOES-5 GCM GOES-5 includes a finite-volume dynamical core and full physics package The model is driven by external data (ARM observations) 40 level model, two control variables: T and Q 10 ensembles, 80 observations of T and Q 40 data assimilation cycles 6-h data assimilation interval Model simulated “observations” with random noise R 1/2 = R 1/2 = 2 GOES-4: Prescribed observation errors directly impact innovation statistics. Possibility for tuning of observation errors. GOES-5: Larger number of DOF in observations is correlated with greater forecast error reduction (measured by the cost function). System’s capability to learn from observations is dependent upon evolving forecast error covariance P f. Acknowledgements This research is partially funded by NASA grants: and NAG References Bishop, C. H., B. J. Etherton, and S. Majumjar, 2001: Adaptive sampling with the ensemble transform Kalman filter. Part 1: Theoretical aspects. Mon. Wea. Rev., 129, 420–436. Rodgers, C. D., 2000: Inverse Methods for Atmospheric Sounding: Theory and Practice. World Scientific, 238 pp. 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] Zupanski, M., 2005: Maximum likelihood ensemble filter: Theoretical aspects. Accepted to Mon. Wea. Rev. [Available at ftp://ftp.cira.colostate.edu/milija/papers/MLEF_MWR.pdf]. Results summary

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