Dynamic Analogue Initialization for Ensemble Forecasting  

作　　者：李珊 容新尧 刘赟 刘征宇 Klaus FRAEDRICH 

机构地区：[1]Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University [2]Chinese Academy of Meteorological Sciences [3]Center for Climatic Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison [4]Max Planck Institute for Meteorology

出　　处：《Advances in Atmospheric Sciences》2013年第5期1406-1420,共15页大气科学进展（英文版）

基　　金：supported by 2012CB955201 and GYHY200906016

摘　　要：This paper introduces a new approach for the initialization of ensemble numerical forecasting： Dynamic Analogue Initialization （DAI）. DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future. As such, DAI performs the ensemble forecast using the best analogues from a full size ensemble. As a pilot study, the Lorenz63 and Lorenz96 models were used to test DAI＇s effectiveness independently. Results showed that DAI can improve the forecast significantly. Especially in lower-dimensional systems, DAI can reduce the forecast RMSE by ~50% compared to the Monte Carlo forecast （MC）. This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error. Meanwhile, a potential improvement of DAI is also proposed, and that is to find the optimal range of embedding time based on the error＇s growing speed.This paper introduces a new approach for the initialization of ensemble numerical forecasting： Dynamic Analogue Initialization （DAI）. DAI assumes that the best model state trajectories for the past provide the initial conditions for the best forecasts in the future. As such, DAI performs the ensemble forecast using the best analogues from a full size ensemble. As a pilot study, the Lorenz63 and Lorenz96 models were used to test DAI＇s effectiveness independently. Results showed that DAI can improve the forecast significantly. Especially in lower-dimensional systems, DAI can reduce the forecast RMSE by ~50% compared to the Monte Carlo forecast （MC）. This improvement is because DAI is able to recognize the direction of the analysis error through the embedding process and therefore selects those good trajectories with reduced initial error. Meanwhile, a potential improvement of DAI is also proposed, and that is to find the optimal range of embedding time based on the error＇s growing speed.

关 键 词：INITIALIZATION ensemble forecast ANALOGUE error growth 

分 类 号：P456[天文地球—大气科学及气象学]