The LAF Approach with 4D Variational Assimilation under Conditions of Model Parameter Errors
To utilize efficiently various types of observational data to improve ensemble forecast,a new forecasting method was put forward in this paper in which lagged average ensemble forecasting(LAF)was made using the initial field acquired by 4DVar with the presence of model parameter errors(LAF-4DVar).Under the conditions of different observational error deviation,observational cases and observational inter-vals,differences were compared between deterministic forecast results which used either the traditional LAF,LAF-4DVar or 4DVar(taking into account the optimum initial field only).As shown in our experiments that the LAF-4DVar made the best forecast in the short term but was close to the traditional LAF with the increase of forecast validation time.
lagged average ensemble forecasting(LAF)4-dimensional variational assimilation(4DVar)parameter errormaximum simplified climate model
万方数据
维普
