Ensemble Kalman filter localization method

Abstract

The present invention belongs to the field of measured marine environment data assimilation, and specifically relates to an ensemble Kalman filter localization method. The present invention comprises: preprocessing measured atmospheric and marine environment data; for each piece of observation data, calculating a priori observation ensemble member of the observation data; calculating the average and variance of an observed priori ensemble; calculating an average observation increment of an ensemble; calculating a perturbed observation increment of each ensemble; projecting an average observation increment of the ensemble onto the ensemble average with a mode state; projecting the perturbed observation increment of each ensemble onto a corresponding ensemble perturbation with the mode state; and obtaining an ensemble member analysis field. According to the method provided by the present invention, the traditional localization method for ensemble Kalman filter is improved, different spatial scales represented by the ensemble average and ensemble perturbation are effectively considered, and the assimilation accuracy of ensemble Kalman filter is significantly improved.

Description

technical field [0001] The invention belongs to the field of measured marine environment data assimilation, and in particular relates to a localization method of an ensemble Kalman filter. Background technique [0002] Ensemble Kalman filtering and four-dimensional variation are two types of advanced data assimilation methods recognized internationally, and they have their own advantages and disadvantages. The biggest advantage of the ensemble Kalman filter over the variational method is that it simulates the prior probability density distribution function of the model state variables through ensemble sampling, and the background error covariance matrix calculated according to the ensemble samples contains the dynamic information of the model, so it is flow dependent. Due to the limitation of computer hardware resources, only a small set of samples (10 2 order of magnitude). For the actual ocean numerical model, the dimension of the state variable is 10 7 , so fewer ense...

AI Technical Summary

Problems solved by technology

Due to the significant difference in the spatial scales of ensemble averaging and ensemble perturbation, traditional localization schemes have obvious limitations

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