Development of a “seamless” mesoscale ensemble data assimilation and prediction system

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Tuesday, 19 January 2010: 8:45 AM
B207 (GWCC)
Yubao Liu, NCAR, Boulder, CO; and W. Wu, F. Vandenberghe, G. Descombes, H. Liu, and T. Warner

Ensemble-based mesoscale data assimilation and probabilistic forecasting are traditionally separated in their developments. However, an accurate forecast of probabilistic distribution functions of state variables is in fact equally important for both ensemble-based data assimilation and probabilistic prediction. Poor sampling and forward propagation of initial states and model uncertainties lead to inaccurate probabilistic forecasts and estimate of background error covariance required for Ensemble Kalman Filter data assimilation (EnKF). Thus a well-formulated ensemble prediction system should theoretically provide more accurate estimate of the forecast error covariance for EnKF. On the other hand, EnKF provides an effective tool for representing the initial condition uncertainties. It should be noted that mesoscale processes are more complicated than global models and may be dominated by physical processes at times. Thus mesoscale models have relatively large errors due to parameterized physical processes with many assumptions. A seamless ensemble data assimilation and probabilistic prediction scheme can address the issues on both aspects.

An innovative mesoscale Ensemble Real-Time Four Dimensional Data Assimilation (E-RTFDDA) and forecasting system has been developed at NCAR. E-RTFDDA contains diverse ensemble perturbation approaches that take into account of uncertainties in all major system components to produce multi-scale continuously-cycling probabilistic data assimilation and forecasting. A 30-member E-RTFDDA system with three nested domains with grid sizes of 30, 10 and 3.33 km has been operating for US Army test ranges since September 2007. In this work, the NCAR DART (Data Assimilation research Testbed) EnKF tools are integrated to E-RTFDDA to form a seamless system . In this system, EnKF takes advantages of E-RTFDDA by deriving error covariance using the multiple perturbation E-RTFDDA forecasts and then it feeds E-RTFDDA forecast with the EnKF mean updates and a subset of EnKF perturbation members. Preliminary results from this seamless system will presented.