Data assimilation method based on adaptive covariance expansion

Abstract

The invention discloses a data assimilation method based on adaptive covariance expansion, and the method comprises the steps of obtaining an atmospheric observation value, and carrying out the mode integration based on a t-1 analysis value, and obtaining a forecast field at an analysis moment t; according to the forecast field set, estimating to obtain an ensemble forecast error covariance matrix Pt and an expansion factor vector at the moment t; updating ensemble members of ensemble Kalman filtering in the assimilation initial process by using the expansion factor vector to increase the ensemble variance of new ensemble Kalman filtering to form new ensemble members, and performing iterative updating on the new ensemble members by using an ensemble Kalman filtering method to obtain final analysis ensemble members; and carrying out pattern forecasting by using analysis set members as initial fields. According to the invention, the expansion factor is adjusted, so that the updated prediction error covariance matrix conforms to the statistical relationship of the prediction error covariance, the information quantity and the observation error covariance, the calculated expansion factor is more reasonable, and the assimilation performance is obviously improved.

Description

technical field [0001] The invention belongs to the technical field of weather forecasting, in particular to a data assimilation method based on self-adaptive covariance expansion. Background technique [0002] From ancient times to the present, no matter how society develops and how dynasties change, people have never stopped exploring nature. From prehistoric humans to modern civilization, people are constantly trying to discover and master the laws of weather development, so as to guide their work and life. Accurate weather forecast has an important impact on human agricultural production, military activities, and daily life. At the beginning of the 20th century, Abbe proposed that the laws of physics could be used for weather forecasting. He realized that predicting the state of the atmosphere can be regarded as an initial boundary value problem in mathematical physics. The basic idea is to solve the problem that can describe Atmospheric hydrodynamics, controlling part...

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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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