Strong coupling data assimilation method combined with optimal observation time window

Abstract

The invention discloses a strong coupling data assimilation method combined with an optimal observation time window, and belongs to the technical field of strong coupling data assimilation and numerical forecasting of a coupling climate mode system. The method comprises the following steps: acquiring a characteristic time scale of a coupling mode state by combining spectral analysis of a couplingmode free integral state, and setting the size of an optimal observation time window according to the time scale of the coupling mode state; On the basis of obtaining effective atmosphere and ocean observation data based on the optimal observation time window, introducing a correlation weight coefficient in the observation window according to an autocorrelation coefficient between atmosphere and ocean states, and reducing time and correlation difference while extracting effective observation information to the maximum extent; According to the method, a coupling correlation weight coefficient is introduced into a traditional strong coupling assimilation scheme based on a steady-state atmosphere and ocean coupling correlation coefficient of a weak coupling assimilation scheme of an optimal observation time window, so that more accurate estimation of the state of the coupling mode is realized, and the numerical forecasting capability of the atmosphere and ocean of the coupling mode is enhanced.

Description

technical field [0001] The invention belongs to the technical field of strong coupling data assimilation and numerical forecasting of a coupled climate model system, and in particular relates to a strong coupling data assimilation method combined with an optimal observation time window. Background technique [0002] The coupled climate model can reasonably simulate the interaction process among various subsystems in the earth system such as the atmosphere, ocean, land and sea ice, so as to simulate 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. The traditional weakly coupled data as...

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Problems solved by technology

On the one hand, in the traditional method, weighting all observations in the observation time window can reduce the calculation cost and reduce the influence of observation errors. The defect is that there is a time difference between the model background field at the time of observation and state estimation in the observation time window. , and there is a difference in the correlation coefficient between the observation corresponding mode state and the background field, the traditional meanization will introduce the above-mentioned time difference and correlation difference, resulting i

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