Method of continuous data assimilation using short-term 4D-VAR analysis

Yang, Shuowen, author
Cotton, William R., author
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A continuous data assimilation method based on short-term four-dimensional variational data assimilation (4D-Var) is proposed. This method consists of forecast and analysis steps. The analysis increment (analyzed value minus forecasted value) is assumed to be proportional to the gradient of a cost function, which measures the misfit between model prediction and observations over a period of time. The gradient of the cost function is calculated with the adjoint method and is updated cyclically. This technique is a kind of retrospective analysis and can continuously assimilate data in an infinite time period. Different forecast model versions (or models) can be used in the forecast and analysis steps. A two-dimensional shallow-water system with horizontal diffusion, Rayleigh friction and external forcing is used to test the proposed method through identical-twin numerical experiments. The control run represents a typical mesoscale case with energy cascaded in two ways (upscale and downscale). The influence of model error and resolution of the analysis grid on the assimilated results is examined. Results show that when model error is small or moderate, the assimilated wind and geopotential fields correlate well with the true fields. When model error is large, the proposed method can still recover a large portion of small-scale motions which are not resolved by observations. Model error can lead to the generation of spurious small-scale gravity waves because of the inconsistency between model and observations. Numerical experiments show that bounding wind divergence and its time tendency can considerably suppress high-frequency spurious gravity waves and improve the assimilated results.
June 30, 1998.
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Meteorology -- Data processing
Dynamic meteorology
Numerical weather forecasting
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