What limits the number of observations that can be effectively assimilated by EnKF
@article{Hotta2020WhatLT, title={What limits the number of observations that can be effectively assimilated by EnKF}, author={Daisuke Hotta and Yoichiro Ota}, journal={arXiv: Data Analysis, Statistics and Probability}, year={2020} }
The ability of ensemble Kalman filter (EnKF) algorithms to extract information from observations is analyzed with the aid of the concept of the degrees of freedom for signal (DFS). A simple mathematical argument shows that DFS for EnKF is bounded from above by the ensemble size, which entails that assimilating much more observations than the ensemble size automatically leads to DFS underestimation. Since DFS is a trace of the posterior error covariance mapped onto the normalized observation…
One Citation
Forecasts of the July 2020 Kyushu Heavy Rain Using a 1000-Member Ensemble Kalman Filter
- Environmental ScienceSOLA
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Forecast performances of the July 2020 Kyushu heavy rain have been revisited with the aim of improving the forecasts for this event. While the Japan Meteorological Agency’s (JMA) deterministic…
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