A coupling data assimilation and parameter optimization method based on an optimal observation time window

Abstract

The invention discloses a coupling data assimilation and parameter optimization method based on an optimal observation time window, and belongs to the technical field of data assimilation, parameter optimization and numerical forecasting of a coupling climate mode system. The method aims to overcome the defects of observation resource utilization and state estimation and parameter optimization precision existing in a traditional coupling data assimilation and parameter optimization method. Characteristic time scales of a coupling mode state are obtained by combining spectral analysis of a coupling mode free integral state, and the size of an optimal observation time window is set according to the time scales of the coupling mode state. Introducing a time weight coefficient in the observation window on the basis of obtaining effective atmosphere and ocean observation data based on the optimal observation time window; Effective observation information is extracted to the maximum extent to fit the characteristic change rate of the coupling mode state, the time-varying characteristics of the internal parameters of the mode are ignored, the time average coefficient in the time window isintroduced, more accurate estimation and optimization of the mode parameters are achieved, and the numerical forecasting capacity of atmosphere and ocean of the coupling mode is enhanced.

Description

technical field [0001] The invention belongs to the technical field of data assimilation, parameter optimization and numerical forecasting of a coupled climate model system, and in particular relates to a coupled data assimilation and parameter optimization method based on an optimal observation time window. Background technique [0002] At present, the coupled climate model can reasonably simulate the interaction process between various subsystems in the earth system such as the atmosphere, ocean, land and sea ice, thereby simulating the climate change process. However, there are often various errors and uncertainties in the coupled model (such as imperfect numerical realization and physical parameterization process, as well as unreasonable model internal parameter values, etc.), which cause the model output to deviate from the real climate characteristics and change process. The actual observation data, thus limiting the climate forecasting ability of the coupled model. T...

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Problems solved by technology

At the same time, the time difference between effective observations in the observation time window will cause overestimation and underestimation of observations at non-assimilative time points, thereby reducing the accuracy of model state and parameter estimation

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