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J. J. Tribbia and D. P. Baumhefner

Abstract

The reliability of reductions of forecasting error derived from changes in the quality of the initial data or model formulation is considered using a signal-to-noise analysis. Defining the initial data error as the data error source and the model error as the modelling source, we propose the use of the modeling error as a baseline against which potential reductions in data error may be calibrated. In the reverse sense, the data error can also be used to calibrate the reduction in the modeling error. A simple nonlinear model is used to illustrate examples of the above reliability test. Further applications of this test to actual numerical forecast experiments using analyses from both the augmented FWE database and the operational NMC data base are shown. Forecast comparisons using various suites of physical parameterizations are also presented.

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J. J. Tribbia and D. P. Baumhefner

Abstract

An examination of the scale interactions in predictability experiments is made using the NCAR Community Climate Model Version 3 (CCM3) at various horizontal resolutions ranging from T42 to T170. Both identical-model and imperfect-model twin experiments are analyzed, and they show distinctive differences from the classical inverse cascade picture of predictability error growth. In the identical-model twin framework, error growth experiments using initial errors confined to long and short scales are compared and contrasted. In these cases, error growth eventually asymptotes to an exponential growth of baroclinically active scales. In the imperfect-model twin experiments, errors rapidly disperse from scales technically beyond model resolution to a small amplitude, spectrally uniform distribution of errors in resolved scales. The errors in resolved scales further amplify in a quasi-exponential growth of the baroclinically active scales. Finally, the implications of these growth mechanisms for the necessary resolution in short- to medium-range numerical weather prediction are given under the assumption that the accuracy of current initial state estimates of the atmosphere remain fixed at their present level.

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