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Comparison of Nonlinear Local Lyapunov Vectors with Bred Vectors, Random Perturbations and ensemble transform kalman filter Strategies in a Barotropic Model

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Advances in Atmospheric Sciences 2016年第9期33卷 1036-1046页

作者： Jie FENG Ruiqiang DING Jianping LI Deqiang LIU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 College of Earth Science University of Chinese Academy of SciencesBeijing 100049 Global Systems Division Earth System Research Laboratory/Oceanic and Atmospheric Research/National Oceanic and Atmospheric AdministrationBoulderCO80305USA Plateau Atmosphere and Environment Key Laboratory of Sichuan Province Chengdu University of Information TechnologyChengdu 610225 College of Global Change and Earth System Sciences Beijing Normal UniversityBeijing 100875 Joint Center for Global Change Studies Beijing 100875 Fujian Meteorological Observatory Fuzhou 350001

The breeding method has been widely used to generate ensemble perturbations in ensemble forecasting due to its simple concept and low computational cost. This method produces the fastest growing perturbation modes to catch the growing components in analysis errors. However, the bred vectors （BVs） are evolved on the same dynamical flow, which may increase the dependence of perturbations. In contrast, the nonlinear local Lyapunov vector （NLLV） scheme generates flow-dependent perturbations as in the breeding method, but regularly conducts the Gram-Schmidt reorthonormalization processes on the perturbations. The resulting NLLVs span the fast-growing perturbation subspace efficiently, and thus may grasp more com- ponents in analysis errors than the BVs. In this paper, the NLLVs are employed to generate initial ensemble perturbations in a barotropic quasi-geostrophic model. The performances of the ensemble forecasts of the NLLV method are systematically compared to those of the random pertur- bation （RP） technique, and the BV method, as well as its improved version--the ensemble transform kalman filter （ETKF） method. The results demonstrate that the RP technique has the worst performance in ensemble forecasts, which indicates the importance of a flow-dependent initialization scheme. The ensemble perturbation subspaces of the NLLV and ETKF methods are preliminarily shown to catch similar components of analysis errors, which exceed that of the BVs. However, the NLLV scheme demonstrates slightly higher ensemble forecast skill than the ETKF scheme. In addition, the NLLV scheme involves a significantly simpler algorithm and less computation time than the ETKF method, and both demonstrate better ensemble forecast skill than the BV scheme.

关键词： ensemble forecasting bred vector nonlinear local Lyapunov vector ensemble transform kalman filter

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Effect of Doubling the ensemble Size on the Performance of ensemble Prediction in the Warm Season Using MOGREPS Implemented at the KMA

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Advances in Atmospheric Sciences 2013年第5期30卷 1287-1302页

作者： Jun Kyung KAY Hyun Mee KIM Young-Youn PARK Joohyung SON Department of Atmospheric Sciences Yonsei University Korea Meteorological Administration

Using the Met Office Global and Regional ensemble Prediction System （MOGREPS） implemented at the Korea Meteorological Administration （KMA）, the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function （PDF）, ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the ensemble transform kalman filter （ETKF） were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.

关键词： ensemble prediction ensemble size ensemble transform kalman filter

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