A New Method to Calculate Nonlinear Optimal Perturbations for Ensemble Forecasting.

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Bibliographic Details
Title:	A New Method to Calculate Nonlinear Optimal Perturbations for Ensemble Forecasting.
Authors:	Ma, Junjie¹ (AUTHOR), Duan, Wansuo² (AUTHOR) duanws@lasg.iap.ac.cn, Liu, Zhuomin³ (AUTHOR), Wang, Ye⁴ (AUTHOR)
Source:	Advances in Atmospheric Sciences. May2025, Vol. 42 Issue 5, p952-967. 16p.
Subject Terms:	Weather forecasting, Orthogonalization, Forecasting, Weather
Abstract (English):	Orthogonal conditional nonlinear optimal perturbations (O-CNOPs) have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events. However, highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting. In this study, we combine a gradient-based iterative idea with the Gram–Schmidt orthogonalization, and propose an iterative optimization method to compute O-CNOPs. This method is different from the original sequential optimization method, and allows parallel computations of O-CNOPs, thus saving a large amount of computational time. We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs. The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method. Moreover, the parallel method significantly reduces the computational time for O-CNOPs. Therefore, the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts. Expectedly, it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events. [ABSTRACT FROM AUTHOR]
Abstract (Chinese):	摘要: 正交条件非线性最优扰动 (O-CNOPs) 方法已被成功应用于产生集合预报样本, 有效提升了高影响天气和气候事件的预报技巧。然而, 高效计算 O-CNOPs 仍是一项挑战。该研究融合梯度迭代优化算法与施密特正交化方法, 通过采用并行算法, 发展了一种高效计算 O-CNOPs 的优化方法。采用著名的 Lorenz-96 模式, 从集合预报技巧和计算效率两方面, 考察了该优化方法较原优化方法的优势。结果表明, 新优化方法不仅成功产生了可靠的集合样本, 获得了与原优化方法相当甚至更高的集合预报技巧, 而且大幅减少了 O-CNOPs 的计算时间。因此, 新优化方法为 O-CNOPs 的高效计算提供了有效途径, 为将 O-CNOPs 方法广泛应用于高影响天气和气候事件的实时集合预报奠定了算法基础。 [ABSTRACT FROM AUTHOR]
Database:	Energy & Power Source

Description
Abstract:	Orthogonal conditional nonlinear optimal perturbations (O-CNOPs) have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events. However, highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting. In this study, we combine a gradient-based iterative idea with the Gram–Schmidt orthogonalization, and propose an iterative optimization method to compute O-CNOPs. This method is different from the original sequential optimization method, and allows parallel computations of O-CNOPs, thus saving a large amount of computational time. We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs. The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method. Moreover, the parallel method significantly reduces the computational time for O-CNOPs. Therefore, the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts. Expectedly, it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events. [ABSTRACT FROM AUTHOR]
ISSN:	02561530
DOI:	10.1007/s00376-024-4069-y