GRAPES全球集合预报系统模式扰动随机动能补偿方案初步探究

为了体现次网格尺度能量升尺度转换过程中存在的不确定性, 文中将随机动能补偿(Stochastic Kinetic Energy Backscatter, SKEB)方案应用于GRAPES(Global/Regional Assimilation and Prediction System)全球集合预报系统(GRAPES-GEPS), 以更好地表征模式误差并且增大集合离散度。使用的SKEB方案基于具有一定时、空相关特征的随机型以及由数值扩散导致的局地动能耗散率来构造随机流函数强迫。并根据流函数与水平风速旋转分量的关系, 将SKEB方案中的流函数强迫转化为适用于GRAPES全球模式的水平风速扰动。结果表明, SKEB方案的使用一方面能够提高GRAPES对大气动能谱的模拟能力; 另一方面能够改善GRAPES-GEPS的集合离散度与集合平均误差的关系, 增加了集合离散度, 并在一定程度上减小了集合平均误差, 尤其是在热带地区这种改进更为显著。而且该方案使得热带地区连续分级概率评分(CRPS评分)显著减小。就降水预报而言, 从Brier评分与相对作用特征面积(AROC, Area under the Relative Operating Characteristics)的结果来看, SKEB方案有助于改善中国地区小雨[0.1 mm, 10 mm)、中雨[10 mm, 25 mm)与大雨[25 mm, 50 mm)量级降水的概率预报技巧, 而对暴雨[50 mm, ∞)量级降水预报技巧影响很小(24 h降水量)。总体上, 模式扰动随机动能补偿方案提高了GRAPES-GEPS的概率预报技巧。 

A stochastic kinetic energy backscatter scheme for model perturbations in the GRAPES global ensemble prediction system

For describing uncertainties in the subgrid-scale energy upscaling transfer, a Stochastic Kinetic Energy Backscatter (SKEB) scheme has been introduced into the Global/Regional Assimilation and Prediction System (GRAPES) global ensemble prediction system (GEPS) to represent model errors more reasonably and increase the ensemble spread. In this research, the SKEB scheme employs the stochastic patterns with temporally and spatially correlated characteristics along with the estimated local kinetic energy dissipation rates caused by numerical diffusion to construct the stochastic stream function forcing. According to the relationship between the streamfunction and the rotational component of horizontal wind, the streamfunction forcing in the SKEB scheme is then transformed into horizontal wind perturbations, which are suitable for the GRAPES global model. The results indicate that, on the one hand, the application of the SKEB scheme improves the simulations of the atmospheric kinetic-energy spectra in the GRAPES model; on the other hand, it leads to a better spread-error relationship, increases the spread of the ensemble and reduces the root mean square error of the ensemble mean to some extent and the improvement is the most pronounced in the tropics. This scheme also contributes to a significant improvement of the continuous rank probability score (CRPS) in the tropics. In terms of precipitation forecast, the results from the Brier score and Area under the Relative Operating Characteristics (AROC) show that the SKEB scheme helps to improve probabilistic forecast skills of rainfall in China for light rain[0.1 mm, 10 mm), moderate rain[10 mm, 25 mm) and heavy rain[25 mm, 50 mm); however, it has little impact on the forecast of rainstorm[50 mm, ∞) (24 h precipitation). On the whole, the introduction of the SKEB scheme ameliorates the probabilistic prediction skills of the GRAPES-GEPS.