Application of scale-selective data assimilation to regional climate modeling and prediction

Abstract

A method referred to as scale-selective data assimilation (SSDA) is designed to inject the large-scale components of the atmospheric circulation from a global model into a regional model to improve regional climate simulations and predictions. The SSDA is implemented through the following procedure: 1) using a low-pass filter to extract the large-scale components of the atmospheric circulation from global analysis or model forecasts; 2) applying the filter to extract the regional-scale and the large-scale components of the atmospheric circulation from the regional model simulations or forecasts; 3) assimilating the large-scale circulation obtained from the global model into the corresponding component simulated by the regional model using the method of three-dimensional variational data assimilation (3DVAR) while maintaining the small-scale components from the regional model during the assimilation cycle; 4) combining the small-scale and the assimilated large-scale components as the adjusted forecasts by the regional climate model and allowing the two components to mutually adjust outside the data assimilation cycle. A case study of summer 2005 seasonal climate hindcasting for the regions of the Atlantic and the eastern United States indicates that the large-scale components from the Global Forecast System (GFS) analysis can be effectively assimilated into the regional model using the scale-selective data assimilation method devised in this study, resulting in an improvement in the overall results from the regional climate model. © 2010 American Meteorological Society.