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An Adaptive Compensation Method for Ensemble Kalman Filter Static Localization Scheme

A Kalman filter and adaptive compensation technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problem of time-consuming, and achieve the effect of improving assimilation accuracy and reducing strong dependence

Active Publication Date: 2017-11-21
HARBIN ENG UNIV +1
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Problems solved by technology

This static localization scheme has obvious limitations, because it is very time-consuming and almost impossible to find an optimal localization factor for the actual 3D ocean numerical model

Method used

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  • An Adaptive Compensation Method for Ensemble Kalman Filter Static Localization Scheme
  • An Adaptive Compensation Method for Ensemble Kalman Filter Static Localization Scheme
  • An Adaptive Compensation Method for Ensemble Kalman Filter Static Localization Scheme

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] The invention provides an adaptive compensation mechanism for the static localization scheme of the ensemble Kalman filter. Including preprocessing the measured data of the atmospheric and oceanic environment; according to the real-time observation system, using the observation error, observation number and significance level of different observation elements to calculate the subsequent threshold to be used; using the ensemble Kalman filter method to sequentially assimilate all Observations; compute posterior ensemble mean and ensemble perturbation for ensemble Kalman filtering; calculate observation margin and update ensemble membership. The invention improves the static localization method in the ensemble Kalman filter, effectively extracts the multi-scale information that cannot be extracted by the static localization method in the observation information, an...

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Abstract

The invention belongs to the field of measured marine environment data assimilation, and in particular relates to an adaptive compensation method for a static localization scheme of an integrated Kalman filter. The present invention includes: preprocessing the measured data of the atmospheric and marine environment; for each observation data, according to the real-time observation system, using the observation error, observation number and significance level of different observation elements, to calculate the threshold value to be used in the follow-up; sequentially Assimilate all observations; calculate their ensemble mean and ensemble perturbation; calculate observation margin and update ensemble membership. The invention improves the static localization method in the ensemble Kalman filter, effectively extracts the multi-scale information that cannot be extracted by the static localization method in the observation information, and significantly improves the assimilation precision of the ensemble Kalman filter.

Description

technical field [0001] The invention belongs to the field of measured marine environment data assimilation, and in particular relates to an adaptive compensation method for a static localization scheme of an integrated 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 o...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 赵玉新吴新荣刘厂付红丽刘利强王喜冬李刚张连新张晓爽张振兴
Owner HARBIN ENG UNIV
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