NDLI: Dynamic analogue initialization for ensemble forecasting

Content Provider	Springer Nature Link
Author	Li, Shan Rong, Xingyao Liu, Yun Liu, Zhengyu Fraedrich, Klaus
Copyright Year	2013
Abstract	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.
Starting Page	1406
Ending Page	1420
Page Count	15
File Format	PDF
ISSN	02561530
Journal	Advances in Atmospheric Sciences
Volume Number	30
Issue Number	5
e-ISSN	18619533
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-08-16
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	initialization ensemble forecast analogue error growth Atmospheric Sciences Meteorology Geophysics/Geodesy
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science

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