2015Unpublished venueRequires access

Sensitivity Analysis of the Sequential Data Assimilation Methods

Han Pe

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Abstract

In order to explore the influence of different assimilation indicators on the sequential data assimilation,in this paper, based on Lorenz-1963 model, we attempt to test the sensitivities of three typical assimilation methods, including En KF, DEn KF and En SRF. We have investigated the effects of different indicators on the data assimilation results. These indicators include the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius. The experimental results show that the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius directly affect the data assimilation result. The improved algorithm based on En KF is superior to the En KF under a given condition. When some preconditions are satisfied, the data assimilation results of all above methods tend to be the same. In practice, this work is of great significance in choosing the optimal sequential assimilation method.

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What this paper is about

In order to explore the influence of different assimilation indicators on the sequential data assimilation,in this paper, based on Lorenz-1963 model, we attempt to test the sensitivities of three typical assimilation methods, including En KF, DEn KF and En SRF. We have investigated the effects of different indicators on the data assimilation results. These indicators include the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius. The experimental results show that the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius directly affect the data assimilation result. The improved algorithm based on En KF is superior to the En KF under a given condition. When some preconditions are satisfied, the data assimilation results of all above methods tend to be the same. In practice, this work is of great significance in choosing the optimal sequential assimilation method.

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Available abstract

In order to explore the influence of different assimilation indicators on the sequential data assimilation,in this paper, based on Lorenz-1963 model, we attempt to test the sensitivities of three typical assimilation methods, including En KF, DEn KF and En SRF. We have investigated the effects of different indicators on the data assimilation results. These indicators include the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius. The experimental results show that the total assimilation time, the assimilation step, the ensemble number, the inflation factor, the observation number and the localization radius directly affect the data assimilation result. The improved algorithm based on En KF is superior to the En KF under a given condition. When some preconditions are satisfied, the data assimilation results of all above methods tend to be the same. In practice, this work is of great significance in choosing the optimal sequential assimilation method.

Key concepts: Data assimilation, Assimilation (phonology), Mathematics, Statistics, Computer science, Econometrics, Algorithm, Meteorology

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