Impact of Parameter Estimation on the Performance of the FSU Global Spectral Model Using Its Full-Physics Adjoint

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The full-physics adjoint of the Florida State University Global Spectral Model at resolution T42L12 is applied to carry out parameter estimation using an initialized analysis dataset. The three parameters, that is, the biharmonic horizontal diffusion coefficient, the ratio of the transfer coefficient of moisture to the transfer coefficient of sensible heat, and the Asselin filter coefficient, as well as the initial condition, are optimally recovered from the dataset using adjoint parameter estimation. The fields at the end of the assimilation window starting from the retrieved optimal initial conditions and the optimally identified parameter values successfully capture the main features of the analysis fields. A number of experiments are conducted to assess the effect of carrying out 4D Var assimilation on both the initial conditions and parameters, versus the effect of optimally estimating only the parameters. A positive impact on the ensuing forecasts due to each optimally identified parameter value is observed, while the maximum benefit is obtained from the combined effect of both parameter estimation and initial condition optimization. The results also show that during the ensuing forecasts, the model tends to “lose” the impact of the optimal initial condition first, while the positive impact of the optimally identified parameter values persists beyond 72 h. Moreover, the authors notice that their regional impacts are quite different.