当前位置: X-MOL 学术Q. J. R. Meteorol. Soc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Stochastically perturbed bred vectors in single‐scale systems
Quarterly Journal of the Royal Meteorological Society ( IF 8.9 ) Pub Date : 2020-08-13 , DOI: 10.1002/qj.3888
Brent Giggins 1 , Georg A. Gottwald 1
Affiliation  

The breeding method is a computationally cheap procedure to generate initial conditions for ensemble forecasting which project onto relevant synoptic growing modes. However, ensembles of bred vectors 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. However, in the case of weakly localised bred vectors, 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.

中文翻译:

单尺度系统中随机扰动的繁殖载体

育种方法是一种计算便宜的过程,可以生成用于整体预报的初始条件,该条件投射到相关的天气生长模式上。但是,繁殖载体的集合通常缺乏多样性,并且与领先的Lyapunov载体一致,这严重影响了它们的统计可靠性。在以前的工作中,我们在多尺度系统的背景下开发了随机扰动的繁殖矢量(SPBV)和随机抽取的繁殖矢量(RDBV)。在这里,我们探讨了何时可以将该方法扩展到没有尺度分离的系统,并研究在单尺度Lorenz'96模型中随机修改的繁殖载体的性能。特别是,我们表明SPBV的性能关键取决于繁殖载体的定位程度。发现与多尺度系统相反,从同化数据初始化时,本地化对于SPBV在没有规模分离的系统中的应用是有害的。但是,在局部繁殖的载体较弱的情况下,SPBV的集合构成了可靠的集合,与传统的繁殖载体相比,具有更高的集合预测技能,同时仍保持了繁殖方法的低计算成本。RDBV被证明具有出色的预测能力,并且在局部弱的情况下可以形成可靠的整体,但是在局部较强的情况下,RDBV不能构成可靠的整体并且过于分散。与传统的繁殖载体相比,SPBV的整体构成了可靠的整体,具有更高的整体预测技能,同时仍保持了繁殖方法的低计算成本。RDBV被证明具有出色的预测能力,并且在局部弱的情况下可以形成可靠的整体,但是在局部较强的情况下,RDBV不能构成可靠的整体并且过于分散。与传统的繁殖载体相比,SPBV的整体构成了可靠的整体,具有更高的整体预测技能,同时仍保持了繁殖方法的低计算成本。RDBV被证明具有出色的预测能力,并且在局部弱的情况下可以形成可靠的整体,但是在局部较强的情况下,RDBV不能构成可靠的整体并且过于分散。
更新日期:2020-08-13
down
wechat
bug