Adjusting the Wind Stress Drag Coefficient in Storm Surge Forecasting Using an Adjoint Technique

Abstract

Abstract A three-dimensional ocean model and its adjoint model are used to adjust the drag coefficient in the calculation of wind stress for storm surge forecasting. A number of identical twin experiments (ITEs) with different error sources imposed are designed and performed. The results indicate that when the errors come from the wind speed, the drag coefficient is adjusted to an “optimal value” to compensate for the wind errors, resulting in significant improvements of the specific storm surge forecasting. In practice, the “true” drag coefficient is unknown and the wind field, which is usually calculated by an empirical parameter model or a numerical weather prediction model, may contain large errors. In addition, forecasting errors may also come from imperfect model physics and numerics, such as insufficient resolution and inaccurate physical parameterizations. The results demonstrate that storm surge forecasting errors can be reduced through data assimilation by adjusting the drag coefficient regardless of the error sources. Therefore, although data assimilation may not fix model imperfection, it is effective in improving storm surge forecasting by adjusting the wind stress drag coefficient using the adjoint technique.