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Data-Based Analysis of DBx using Statistical Interpolation

Statistical interpolation combines observations with a background model to globally improve the estimated state of the magnetic field. This study focuses on estimating error correlations in the B-field model used. Error correlations are numerically estimated by perturbing model inputs and parameters. The figure displays predicted DBx at the position of GOES-12 computed from the Tsyganenko (2001) field model and GOES-10 data, with error correlation coefficients shown.

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Data-Based Analysis of DBx using Statistical Interpolation

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  1. Progress in LWS TR&T Research June 2004 Steve Naehr, Frank Toffoletto Rice University

  2. Data-based analysis of DBx at GEO Statistical interpolation combines observations with a background model, to improve the estimated state globally. The figure shows predicted DBx at the position of GOES-12, computed from the Tsyganenko (2001) field model and data from GOES-10.

  3. Estimating error correlations in the Tsyganenko (2001) B-field model Statistical interpolation requires error structure of background model. Here, error correlations are estimated numerically, by perturbing model inputs and parameters. Left: contours of DBz error correlation coefficient, relative to point marked “+” in equatorial plane.

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