Anisotropic Correlation Functions for Objective Analysis

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  • 1 Mathematics Department, Dalhousie University, Halifax, Nova Scotia, Canada
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Abstract

Covariance models used in the data assimilation step of operational forecasting generally assume isotropy of height field correlations on constant pressure levels. Because of the evidence that this assumption is a significant source of forecast error, especially In regions of low density data, a two-dimensional anisotropic correlation model has been derived. Using a simple autoregressive scheme, cumbersome extension of the modeling problem has been avoided and much of the direction-dependent variability of observed statistics is resolved. Compared to deviations of observed correlation values around the best fitting isotropic model, the residual variance has been reduced by 56&%.

Abstract

Covariance models used in the data assimilation step of operational forecasting generally assume isotropy of height field correlations on constant pressure levels. Because of the evidence that this assumption is a significant source of forecast error, especially In regions of low density data, a two-dimensional anisotropic correlation model has been derived. Using a simple autoregressive scheme, cumbersome extension of the modeling problem has been avoided and much of the direction-dependent variability of observed statistics is resolved. Compared to deviations of observed correlation values around the best fitting isotropic model, the residual variance has been reduced by 56&%.

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