Ma, J. J., W. S. Duan, Z. M. Liu, and Y. Wang, 2025: A new method to calculate nonlinear optimal perturbations for ensemble forecasting. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-024-4069-y.
Citation: Ma, J. J., W. S. Duan, Z. M. Liu, and Y. Wang, 2025: A new method to calculate nonlinear optimal perturbations for ensemble forecasting. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-024-4069-y.

A New Method to Calculate Nonlinear Optimal Perturbations for Ensemble Forecasting

  • 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.
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