Rapid collective Kalman filtering assimilating method for real-time data of high-frequency observation data

Abstract

The invention relates to a rapid collective Kalman filtering assimilating method for real-time data of high-frequency observation data. The method comprises: collecting the high-frequency observation data and controlling the quality; calculating an observation error covariance matrix; obtaining the error covariance matrix of background fields by calculating a forecast trend, i.e. the difference value of the adjacent background fields; utilizing the covariance matrix, the error covariance matrix of the background fields, the observation data and the background fields currently obtained by the calculation of a marine numerical model so as to carry out the real-time assimilation on the observation data of different moments, assigning the updated analysis field to the initial field of the next-moment integral and continuously forecasting forwards; and repeating the operations, thus realizing the real-time assimilation on the high-frequency observation data of different moments in the integral course. The assimilating method has the advantages that the real-time assimilation of the high-frequency observation data is realized; the assimilation efficiency of the data is enhanced; the defect that a large amount of collective models are simultaneously operated in the implementation course of the traditional EnKF (ensemble kalman filter) is overcome; the problem of non-convergence is avoided; and the purposes of accurate numerical simulation and marine forecasting are reached.

Description

technical field [0001] The invention relates to a method for real-time assimilation of high-frequency data, in particular to a method for assimilating fast ensemble Kalman filter for real-time data of high-frequency observation data, and belongs to the fields of numerical prediction of marine elements and numerical simulation of marine engineering. Background technique [0002] Currently, marine resources are in urgent need of development and management. The change of marine climate accompanied by global climate change has made various marine disasters more frequent, and marine environmental pollution incidents such as ship oil spill accidents and red tide disasters have also occurred frequently. Therefore, it is of great significance to study global climate change and offshore marine disasters, forecast climate change and offshore marine disasters, and forecast the distribution and movement path of marine pollutants. To achieve accurate ocean forecasting in a wide range of...

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Problems solved by technology

For accurate ocean forecasting, this is a key problem that needs to be solved urgently. However, although the existing ocean data assimilation methods, such as the Kalman filter method and the variational method, are perfect in theory, they have problems in the actual operation process. Due to their respective limitations, it is impossible to achieve high-frequency, real-time, and rapid assimilation of radar flow field data, which has become a bottleneck for accurate ocean forecasting

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