随机扰动育种载体
Stochastically perturbed bred vectors
摘要：
育种方法是一个计算成本低的过程，以产生集合预报的初始条件，项目到相关的天气增长模式。然而，繁殖向量的集合往往缺乏多样性，并与领先的李雅普诺夫向量对齐，这严重影响了它们的统计可靠性。在之前的工作中，我们在多尺度系统的背景下开发了随机扰动繁殖向量（SPBV）和随机抽取繁殖向量（RDBV）。在这里，我们探讨了这种方法可以扩展到没有尺度分离的系统，并检查随机修改繁殖向量的性能在单尺度洛伦兹96模型。特别是，我们表明，SPBV的性能关键取决于繁殖载体的本地化程度。据发现，相反的情况下，多尺度系统，本地化是不利的应用程序的SPBV系统中没有尺度分离时，从同化数据初始化。然而，在弱本地化的繁殖载体的情况下，与经典的繁殖载体相比，SPBV的集合构成了具有改进的集合预测技能的可靠集合，同时仍然保留了繁殖方法的低计算成本。RDBV具有优越的预测能力，并形成一个可靠的合奏弱本地化的情况下，但在情况下，当他们强烈本地化，他们不构成一个可靠的合奏，是过度分散。
英文摘要：
The breeding method is a computationally cheap procedure to generate initial conditions for ensemble forecasting which project onto relevant synoptic growing modes. Ensembles of bred vectors, however, often lack diversity and align with the leading Lyapunov vector, which severely impacts their statistical reliability. In previous work we developed stochastically perturbed bred vectors (SPBVs) and random draw bred vectors (RDBVs) in the context of multi-scale systems. Here we explore when this method can be extended to systems without scale separation, and examine the performance of the stochastically modified bred vectors in the single scale Lorenz 96 model. In particular, we show that the performance of SPBVs crucially depends on the degree of localisation of the bred vectors. It is found that, contrary to the case of multi-scale systems, localisation is detrimental for applications of SPBVs in systems without scale-separation when initialised from assimilated data. In the case of weakly localised bred vectors, however, ensembles of SPBVs constitute a reliable ensemble with improved ensemble forecasting skills compared to classical bred vectors, while still preserving the low computational cost of the breeding method. RDBVs are shown to have superior forecast skill and form a reliable ensemble in weakly localised situations, but in situations when they are strongly localised they do not constitute a reliable ensemble and are over-dispersive.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
