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This section presents a twin experiment aimed for getting more understanding about the data assimilation under an idealized situation. In reality, model noise as well as the true state of a system is unknown. One needs to make certain assumptions in order to apply a data assimilation technique. A twin experiment allows us to study the effect of various assumptions on the behaviour of a data assimilation system. The goal of this first twin experiment is to check if the data assimilation can improve the estimate of all model state variables for the Yeongsan EFDC model in an ideal case where all error statistics are known.
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- Algal Cyanobacteria and Algal Green: the truth is non zero even in the winter. Is this realistic? This is the result of modelling the error as additive to the input variable. Perhaps modelling the error with "ln" operation, i.e. the noise process determines the fraction of the input that is noisy; the perturbed input is computed according to: input = input * exp(noise) ≈ input * (1+noise) ? Input from NIER (24-11-2016): This is indeed unrealistic. NIER will study this suggestion.
At validation stations
The accuracy at all assimilation and validation stations is presented in term of RMSE and shown in the figures below. At the x-axes, the locations are arranged from upstream to downstream. The assimilation station is location 5004A10.
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