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Seung-Jong Baek, Istvan Szunyogh, Brian R. Hunt, and Edward Ott

somewhat different for the temperature. This variable behaves similarly to the wind at and below the 300-hPa levels, but at the higher model levels the adaptive bias correction increases the bias ( Fig. 15 ), and consequently, the root-mean square error (not shown). This behavior of the global bias is due to problems in the tropics ( Fig. 16 ), while the temperature in the extratropics (not shown) behaves similarly to the wind. A more careful examination of the cause of the increased temperature bias

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Junjie Liu, Hong Li, Eugenia Kalnay, Eric J. Kostelich, and Istvan Szunyogh

Hemisphere (SH). As pointed out in section 3 , this improvement comes from the impact of the observations of the other dynamical variables through the background error covariance terms. However, this impact is not always positive, especially in the tropics, which may be due to the strong convective activities and the difficulty to estimate accurate background error covariance between humidity and the other dynamical variables in that region. One common characteristic of these three comparisons is the

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Olivier Pannekoucke

. The 𝗕 matrix quantifies statistical information evolving with time along the analysis/forecast cycles. It is known that horizontal and vertical correlations vary geographically ( Lönnberg 1988 ). In particular, horizontal scales tend to be broader in the tropics than at high latitudes because of atmospheric dynamics ( Ingleby 2001 ). There are also different horizontal correlations at different levels, and different vertical correlations for different horizontal wavenumbers. The latter property

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