Why does EnKF suffer from analysis overconfidence? An insight into exploiting the ever?increasing volume of observations
نویسندگان
چکیده
The ability of ensemble Kalman filter (EnKF) algorithms to extract information from observations is analyzed with the aid concept degrees freedom for signal (DFS). A simple mathematical argument shows that DFS EnKF bounded above by size, which entails assimilating much more than size automatically leads underestimation. Since a trace posterior error covariance mapped onto normalized observation space, underestimated implies overconfidence (underdispersion) in analysis spread, which, cycled context, requires inflation be applied. theory then extended cases where localization schemes (either B-localization or R-localization) are applied show how they alleviate underestimation issue. These findings demonstrated one-dimensional model. Finally, used form speculative arguments about interpret several puzzling features LETKF previously reported literature such as why using less can lead better performance, when optimal scales tend occur, and methods based on relaxation prior approach particularly successful inhomogeneously distributed. presumably first application diagnostics quasi-operational global system presented Appendix.
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ژورنال
عنوان ژورنال: Quarterly Journal of the Royal Meteorological Society
سال: 2021
ISSN: ['1477-870X', '0035-9009']
DOI: https://doi.org/10.1002/qj.3970