Storm-scale ensemble forecast based on breeding of growth modes

摘要

How to obtain fast-growth errors,which is comparable to the actual forecast growth error,is a crucial problem in ensemble fore-cast(EF).The method,Breeding of Growth Modes(BGM),which has been used to generate perturbations for medium-range EF at NCEP,simulates the development of fast-growth errors in the analysis cycle,and is a reasonable choice in capturing growing errors modes,especially for extreme weather by BGM.An ideal supercell storm,simulated by Weather Research Forecast model(WRF),occurred in central Oklahoma on 20 May 1977.This simulation was used to study the application of BGM methods in the meso-scale strong convective Ensemble Prediction System(EPS).We compared the forecasting skills of EPS by different pertu-bation methods,like Monte-Carlo and BGM.The results show that the ensemble average forecast based on Monte-Carlo with sta-tistics meaning is superior to the single-deterministic prediction,but a less dynamic process of the method leads to a smaller spread than expected.The fast-growth errors of BGM are comparable to the actual short-range forecast error and a more appropri-ate ensemble spread.Considering evaluation indexes and scores,the forecast skills of EPS by BGM is higher than Monte-Carlo's.Furthermore,various breeding cycles have different effects on precipitation and non-precipitation fields,confirmation of reason-able cycles need consider balance between variables.