Data Assimilation Method Based on Adaptive Covariance Inflation

Abstract

The invention discloses a data assimilation method based on adaptive covariance expansion. The method includes the steps of: obtaining atmospheric observation values, based on t‑ The analysis value of 1 is model-integrated to obtain the analysis time t forecast field; according to the set of forecast fields, it is estimated that t Ensemble forecast error covariance matrix P t and the expansion factor vector; the expansion factor vector is updated to the set members of the ensemble Kalman filter in the initial process of assimilation, so that the set variance of the new ensemble Kalman filter is increased to form a new set member, and the new ensemble Kalman filter method is used to update The ensemble members are iteratively updated to obtain the final analysis ensemble members; the analysis ensemble members are used as the initial field for model prediction. The invention adjusts the expansion factor so that the updated forecast error covariance matrix conforms to the statistical relationship of forecast error covariance, innovation amount and observation error covariance, the calculated expansion factor is more reasonable, and the assimilation performance is obviously improved.

Description

Technical field [0001] The present invention belongs to the field of weather forecasting, and more particularly to data assimilation methods based on adaptive covary difference. Background technique [0002] From ancient times, regardless of how the society develops, the dynasty is replaced, and people have never stopped natural exploration. From prehistoric human to modern civilization, people are constantly trying to discover and master the development rules of the weather, to guide their work and life. Accurate weather forecast has an important influence on human agricultural production, military activities, and daily life. At the beginning of the twentieth century, Abbe proposed that physical law can be used in weather forecast. He realized that the predicted atmospheric state can be regarded as an initial boundary value problem of mathematics physics, its basic idea is based on the currently observed weather, by solving energy description Atmospheric fluid mechanics, the con...

AI Technical Summary

Problems solved by technology

In 2009, Anderson pointed out that in the global weather forecast system, observations in North America are relatively dense. Due to the existence of model errors and sampling errors, the variance may be underestimated. However, in South America, where observations are relatively sparse, if the same expansion The method will also increase the variance of model variables in South America, resulting in inconsistency with the climate state, and more seriously, it will destroy the physical balance of the model field and cause the model results to be unusable

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