| The National Meteorological Center's spectral statistical interpolation analysis system - :11449 | National Weather Service (NWS)
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The National Meteorological Center's spectral statistical interpolation analysis system
• Published Date:
1991
[PDF - 2.35 MB]

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National Meteorological Center (U.S.)
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At the National Meteorological Center (NMC), a new analysis system is being extensively tested for possible use in the operational Global Data Assimilation System. This analysis system is called the Spectral Statistical Interpolation (SS1) Analysis system because the spectral coefficients used in the NMC spectral model are analysed directly using the same basic equations as statistical (optimal) interpolation. Results from several months of parallel testing with the NMC spectral model have been very encouraging. Favorable features include smoother analysis increments, greatly reduced changes from initialization, and significant improvement of 1-5 day forecasts. Although the analysis is formulated as a variational problem, the objective function being minimized is formally the same one that forms the basis of all existing optimal interpolation schemes. This objective function is a combination of forecast and observation deviations from the desired analysis, weighted by the inverses of the corresponding forecast and observation error covariance matrices. There are two principal differences in how the SSI implements the minimization of this functional as compared to the current OI used at NMC. First, the analysis variables are spectral coefficients instead of gridpoint values. Second, all observations are used at once to solve a single global problem. No local approximations are made, and there is no special data selection. Because of these differences, it is straightforward to include unconventional data, such as radiances, in the analysis. Currently temperature, wind, surface pressure, mixing ratio and SSM/I total precipitable water are used as the observation variables. Soon to be added is the scatterometer surface winds. In this paper, we provide a detailed description of the SSI and present a few results.

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