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Rosangela Saher Cintra and Steve Cocke



Data assimilation is the process by which measurements and model predictions are combined to obtain an accurate representation of the state of the modeled system. We implemented a data assimilation scheme called LETKF (local ensemble transform Kalman filter) with FSUGSM (Florida State University Global Spectral Model) and made an experiment to evaluate the initial condition generated to numerical weather prediction to FSUGSM model. The LETKF analysis carries out independently at each grid point with the use of “local” observations. An ensemble of estimates in state space represents uncertainty. The FSUGSM is a multilevel (27 vertical levels) spectral primitive equation model, where the variables are expanded horizontally in a truncated series of spherical harmonic functions (at resolution T63) and a transform technique is applied to calculate the physical processes in real space. The assimilation cycle runs on the period 01/01/2001 to 31/01/2001 at (00, 06, 12 and 18 GMT) for each day. We examined the atmospheric fields during the period and the OMF (observation-minus-forecast) and the OMA (observation-minus-analysis) statistics to verify the analysis quality comparing with forecasts and observations. The analyses present stability and show suitable to initiate the weather predictions.


Data assimilation, Kalman filter, numerical weather prediction, global atmospheric model

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