Ensemble Data Assimilation without Perturbed Observations

作者：Jeffrey S. Whitaker, Thomas M. Hamill · 发表于：Monthly Weather Review · 年份：2002 · DOI：10.1175/1520-0493(2002)130<1913:edawpo>2.0.co;2 · 被引用次数：1617 · 研究领域：Meteorological Phenomena and Simulations、Climate variability and models、Atmospheric and Environmental Gas Dynamics

The ensemble Kalman filter (EnKF) is a data assimilation scheme based on the traditional Kalman filter update equation. An ensemble of forecasts are used to estimate the background-error covariances needed to compute the Kalman gain. It is known that if the same observations and the same gain are used to update each member of the ensemble, the ensemble will systematically underestimate analysis-error covariances. This will cause a degradation of subsequent analyses and may lead to filter divergence. For large ensembles, it is known that this problem can be alleviated by treating the observations as random variables, adding random perturbations to them with the correct statistics. Two important