Impact of parameter estimation on the performance of the FSU Global Spectral Model using its full-physics adjoint

Thr full-physics adjoint of the Florida State University Global Spectral Mo del at resolution T42L12 is applied to carry our parameter estimation using an initialized analysis dataset. The three parameters, that is, the biharm onic horizontal diffusion coefficient, the ratio of the transfer coefficien t of moisture to the transfer coefficient of sensible heat, and the Asselin filter coefficient. as well as the initial condition, are optimally recove red from the dataset using adjoint parameter estimation. The fields at the end of the assimilation window starting from the retrieve d 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 as ess the effect of carrying out SD Var assim ilation 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 dentified parameter value is observed, whi le the maximum benefit is obtained from the combined effect of both paramet er estimation and initial condition optimization. The results also show tha t during the ensuing forecasts, the model tends to "lose" the impact of the optimal initial condition first, while the positive impact of the optimall y identified parameter values persists beyond 72 h. Moreover. the authors n otice that their regional impacts are quite different.