The singular vector (SV) initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model (TLM). Based on the global tangent linear and adjoint model of GRAPES-GEPS (Global/Regional Assimilation and Prediction System—Global Ensemble Prediction System), some experiments were carried out to analyze the structure of the moist SVs from the perspectives of the energy norm, energy spectrum, and vertical structure. The conclusions are as follows: The evolution of the SVs is synchronous with that of the atmospheric circulation, which is flow-dependent. The moist and dry SVs are located in unstable regions at mid-to-high latitudes, but the moist SVs are wider, can contain more small- and medium-scale information, and have more energy than the dry SVs. From the energy spectrum analysis, the energy growth caused by the moist SVs is reflected in the relatively small-scale weather system. In addition, moist SVs can generate perturbations associated with large-scale condensation and precipitation, which is not true for dry SVs. For the ensemble forecasts, the average anomaly correlation coefficient of large-scale circulation is better for the forecast based on moist SVs in the Northern Hemisphere, and the low-level variables forecasted by the moist SVs are also improved, especially in the first 72 h. In addition, the moist SVs respond better to short-term precipitation according to statistical precipitation scores based on 10 cases. The inclusion of the large-scale condensation process in the calculation of SVs can improve the short-term weather prediction effectively.

中文翻译：

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湿奇异向量在GRAPES-GEPS中的应用及特性分析

奇异向量 (SV) 初始扰动方法可以捕获切线线性模型 (TLM) 中增长最快的初始扰动。基于GRAPES-GEPS（Global/Regional Assimilation and Prediction System—Global Ensemble Prediction System）的全局切线线性和伴随模型，从能量范数、能量谱和垂直结构。结论如下： SVs的演化与大气环流的演化是同步的，具有流量依赖性。湿和干SVs位于中高纬度不稳定区域，但湿SVs比干SVs更宽，可以包含更多的中小尺度信息，具有更多的能量。从能谱分析，潮湿 SV 引起的能量增长反映在相对较小的天气系统中。此外，潮湿的 SV 会产生与大规模冷凝和降水相关的扰动，而干燥的 SV 则不然。对于集合预报，大尺度环流的平均异常相关系数在北半球基于潮湿SV的预报中较好，潮湿SV预报的低层变量也有所改善，尤其是前72 h . 此外，根据基于 10 个案例的统计降水评分，潮湿的 SVs 对短期降水的反应更好。在SVs的计算中加入大尺度凝结过程可以有效地提高短期天气预报。