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An ensemble Kalman filter (EnKF) is used to assimilate data onto a non-linear chaotic model, coupling two kinds of variables. The first kind of variables of the system is characterized as large amplitude, slow, large scale, distributed in eight equally spaced locations around a circle. The second kind of variables are small amplitude, fast, and short scale, distributed in 256 equally spaced locations. Synthetic observations are obtained from the model and the observational error is proportional to their respective amplitudes. The performance of the EnKF is affected by differences in the spatial correlation scales of the variables being assimilated. This method allows the simultaneous assimilation of all the variables. The ensemble filter also allows assimilating only the large-scale variables, letting the small-scale variables to freely evolve. Assimilation of the large-scale variables together with a few small-scale variables significantly degrades the filter. These results are explained by the spurious correlations that arise from the sampled ensemble covariances. An alternative approach is to combine two different initialization techniques for the slow and fast variables. Here, the fast variables are initialized by restraining the evolution of the ensemble members, using a Newtonian relaxation toward the observed fast variables. Then, the usual ensemble analysis is used to assimilate the large-scale observations. 相似文献
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Observation bias correction with an ensemble Kalman filter 总被引：1，自引：0，他引：1
ELANA J. FERTIG SEUNG-JONG BAEK BRIAN R. HUNT EDWARD OTT ISTVAN SZUNYOGH JOSÉ A. ARAVÉQUIA EUGENIA KALNAY HONG LI JUNJIE LIU 《地球，A辑:动力气象学与海洋学》2009,61(2):210-226
This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme—the local ensemble transform Kalman filter (LETKF)—to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations. 相似文献
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Improved Analyses and Forecasts With AIRS Temperature Retrievals Using the Local Ensemble Transform Kalman Filter 总被引：1，自引：1，他引：0
In this paper we investigate the impact of the Atmospheric Infra-Red Sounder (AIRS) temperature retrievals on data assimilation and the resulting forecasts using the four-dimensional Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme and a reduced resolution version of the NCEP Global Forecast System (GFS). Our results indicate that the AIRS temperature retrievals have a significant and consistent positive impact in the Southern Hemispheric extratropics on both analyses and forecasts, which is found not only in the temperature field but also in other variables. In tropics and the Northern Hemispheric extratropics these impacts are smaller, but are still generally positive or neutral. 相似文献
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Assessing a local ensemble Kalman filter: perfect model experiments with the National Centers for Environmental Prediction global model 总被引：1，自引：0，他引：1
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Four-dimensional ensemble Kalman filtering 总被引：3，自引：0，他引：3
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