摘要：A simple and computationally efficient method is presented for creating a high-resolution regional (child) model nested within a coarse-resolution, good-quality data-assimilating (parent) model. The method, named Nesting with Downscaling and Data Assimilation (NDA), reduces bias and root mean square errors (RMSE) of the child model and does not allow the child model to drift from reality. Usually coarser resolution models, e.g., global scale, are used to provide boundary conditions for the nested child model. The basic idea of the NDA method is to use a complete 3D set of output data from the parent model using a process which is similar to data assimilation of observations into an ocean model. In this way, the child model is physically aware of observations via the parent model. The method allows for avoiding a complex process of assimilating the same observations which were already assimilated into the parent model. The NDA method is illustrated in several simple 2D synthetic cases where the true solution is known. The NDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE, typically by a factor of two to five, occasionally more.
