Local Ensemble Transform Kalman Filtering with an AGCM at a T159/L48 Resolution

A local ensemble transform Kalman filter (LETKF) is developed and assessed with the AGCM for the Earth Simulator at a T159 horizontal and 48-level vertical resolution (T159/L48), corresponding to a grid of 480 240 48. Following the description of the LETKF implementation, perfect model Observing Systems Simulation Experiments (OSSEs) with two kinds of observing networks and an experiment with real observations are performed. First, a regular observing network with approximately 1% observational coverage of the system dimension is applied to investigate computational efficiency and sensitivities with the ensemble size (up to 1000) and localization scale. A 10-member ensemble is large enough to prevent filter divergence. Using 20 or more members significantly stabilizes the filter, with the analysis errors less than half as large as the observation errors. There is nonnegligible dependence on the localization scale; tuning is suggested for a chosen ensemble size. The sensitivities of analysis accuracies and timing on the localization parameters are investigated systematically. A computational parallelizing ratio as large as 99.99% is achieved. Timing per analysis is less than 4 min on the Earth Simulator, peak performance of 64 GFlops per computational node, provided that the same number of nodes as the ensemble size is used, and the ensemble size is less than 80. In the other set of OSSEs, the ensemble size is fixed to 40, and the real observational errors and locations are adapted from the Japan Meteorological Agency’s (JMA’s) operational numerical weather prediction system. The analysis errors are as small as 0.5 hPa, 2.0 m s 1 , and 1.0 K in major areas for sea level pressure, zonal and meridional winds, and temperature, respectively. Larger errors are observed in data-poor regions. The ensemble spreads capture the actual error structures, generally representing the observing network. However, the spreads are larger than the actual errors in the Southern Hemisphere; the opposite is true in the Tropics, which suggests the spatial dependence of the optimal covariance inflation. Finally, real observations are assimilated. The analysis fields look almost identical to the JMA operational analysis; 48-h forecast experiments initiated from the LETKF analysis, JMA operational analysis, and NCEP–NCAR reanalysis are performed, and the forecasts are compared with their own analyses. The 48-h forecast verifications suggest a similar level of accuracy when comparing LETKF to the operational systems. Overall, LETKF shows encouraging results in this study.

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