Bayesian fitering-based general data assimilation method

Abstract

The invention discloses a bayesian fitering-based general data assimilation method. The method comprises the steps of: inputting an initial value set into an analysis model in a prediction step so as to obtain a prediction set value; calculating prediction error covariance matrix by using set kalman filtering in an updating step, and updating each prediction set according to the observation value and kalman gain matrix; or, calculating importance weight of each set sample by adopting particle filtering through set prediction value, calculating the number of effective particles by utilizing normalization importance, resampling the set according to the weight to obtain updated analysis value and analysis set; or, calculating prediction error covariance matrix by adopting unscented kalman filtering, and updating each prediction set according to the observation value and kalman gain matrix; conducting next prediction and assimilation by taking the updated analysis set as the initial values of the analysis model, and repeating the prediction step and the updating step. The method can enable Earth remote-sensing observation information and land surface process model information to be effectively integrated, thus forming a land surface process information prediction system with small errors.

Description

technical field

[0001] The present invention relates to the field of earth system scientific information processing, in particular to a general data assimilation algorithm based on Bayesian filtering, which enables the effective fusion of earth remote sensing observation information and land surface process model information, thereby forming an Forecasting system for land surface process information (such as soil moisture, soil temperature, etc.). Background technique

[0002] The core idea of ​​land surface data assimilation is to integrate the direct and indirect observations from different sources and different resolutions through the data assimilation algorithm within the dynamic framework of the land surface process model, and combine the land surface process model with various observation operators (such as radiation Transmission model) is integrated into a forecast system that continuously relies on observations to automatically adjust the model trajectory and reduce ...

Examples

Embodiment Construction

[0076] First, it is theoretically demonstrated that Bayesian theory is the cornerstone of data assimilation:

[0077] Bayesian theory provides a unified methodology for sequential filtering of linear and nonlinear systems with noise, thus providing a broader theoretical basis for data assimilation. The invention uses the language of data assimilation and the standard expression form to analyze the data assimilation in the nonlinear system from the angle of Bayesian filtering.

[0078] 1.1) Data assimilation and nonlinear dynamic system

[0079] The state-space method provides a unified framework for describing the state estimation problem of a dynamical system. It is divided into a state prediction model and an observation model, which are also called model operators and observation operators in the data assimilation system.

[0080] Among them, the nonlinear prediction model (namely, the model operator) of the state space is expressed as:

[0081] x t (t k ) = M k (X t ...

technical field

[0001] The present invention relates to the field of earth system scientific information processing, in particular to a general data assimilation algorithm based on Bayesian filtering, which enables the effective fusion of earth remote sensing observation information and land surface process model information, thereby forming an Forecasting system for land surface process information (such as soil moisture, soil temperature, etc.). Background technique

[0002] The core idea of ​​land surface data assimilation is to integrate the direct and indirect observations from different sources and different resolutions through the data assimilation algorithm within the dynamic framework of the land surface process model, and combine the land surface process model with various observation operators (such as radiation Transmission model) is integrated into a forecast system that continuously relies on observations to automatically adjust the model trajectory and reduce ...

Examples

Embodiment Construction

[0076] First, it is theoretically demonstrated that Bayesian theory is the cornerstone of data assimilation:

[0077] Bayesian theory provides a unified methodology for sequential filtering of linear and nonlinear systems with noise, thus providing a broader theoretical basis for data assimilation. The invention uses the language of data assimilation and the standard expression form to analyze the data assimilation in the nonlinear system from the angle of Bayesian filtering.

[0078] 1.1) Data assimilation and nonlinear dynamic system

[0079] The state-space method provides a unified framework for describing the state estimation problem of a dynamical system. It is divided into a state prediction model and an observation model, which are also called model operators and observation operators in the data assimilation system.

[0080] Among them, the nonlinear prediction model (namely, the model operator) of the state space is expressed as:

[0081] x t (t k ) = M k (X t ...

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