Scaling properties of meteorological analyses, numerical models, and atmospheric fields

Details Scaling properties of meteorological analyses, numerical models, and atmospheric fields Scaling properties of meteorological analyses, numerical models, and atmospheric fields

Dec 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007agufm.u43b1132s&link_type=abstract

American Geophysical Union, Fall Meeting 2007, abstract #U43B-1132

Mathematics

Logic

4475 Scaling: Spatial And Temporal (1872, 3270, 4277)

Scientific paper

Between the outer planetary scale and inner (viscous) dissipation scale, the basic equations of the atmosphere have no characteristic lengths. We therefore expect that both the atmosphere and the corresponding numerical (weather/climate) models should be scaling; i.e., that their statistics (such as spectra) are power law functions of space/time scales. This expectation has been repeatedly confirmed by empirical observations, and most recently and spectacularly, by the systematic analysis of TRMM satellite data which include radar-reflectivity, visible, near and far infrared, and passive microwave channels. While the temporal scaling properties of climate models have been occasionally studied, the model spatial resolutions have been too low to allow systematic study of their spatial scaling properties. However, in the last few years, the models have become large enough (i.e., they contain a wide enough range of spatial scales) so that their spatial scaling properties can be reasonably well determined using a variety of analysis techniques. It is therefore possible to evaluate model performance not only in the usual deterministic sense of comparing a model realization and an atmospheric "snapshot", but also by making a stochastic evaluation by comparing their scale-by-scale statistical properties. This overcomes many of the problems of inadequate data which plague attempts to evaluate performance on individual realizations. Indeed, by systematically studying the scaling characteristics of the empirical data, the analyses, and then the model integrations, we can examine the "stochastic coherence" of the data assimilation and model system. We investigate this problem by considering both temporal and spatial scaling in the ERA-40 (ECMWF reanalysis) and CMC GEMS (Canadian Meteorological Centre Global Environmental Multi-Scale) model.

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