Sub-seasonal to Seasonal Hindcasts of Stratospheric Sudden Warming by BCC_CSM1.1(m):A Comparison with ECMWF

This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM(ECMWF) is the hindcast initiated two(three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.     

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。      

中尺度降水集合预报随机参数扰动方法敏感性试验

中尺度降水模式预报具有很大的不确定性,为更好地描述与模式降水预报密切相关的物理过程关键参数的不确定性,基于中国气象局GRAPES（Global/Regional Assimilation and Prediction System）中尺度区域集合预报模式,从对模式降水预报不确定性有较大影响的积云对流、云微物理、边界层及近地面层等4个参数化方案中选取了18个关键参数,设计了一种随机参数扰动方案（Stochastically Perturbed Parameterization,SPP）,并通过2015年6—7月总计10 d的随机扰动集合预报试验,对比分析了SPP方案对不同物理过程参数扰动敏感性、随机场时、空尺度敏感性、能量变化特征及其集合预报效果。结果显示,对所选择的任一物理过程参数化方案增加SPP扰动后,降水及等压面要素的概率预报技巧优于无SPP扰动的预报,而扰动积云对流和边界层过程中的参数较扰动云微物理过程中的参数影响更显著,且同时扰动积云对流、云微物理、边界层及近地面层参数化方案中的18个参数的集合预报效果优于扰动任何单一物理过程中的部分参数,表明SPP方案能够有效地提高中尺度降水概率预报技巧;从能量变化特征可知,不同物理过程的参数扰动对动能、内能和总能量的影响层次和特征有所不同,但总体而言,扰动前后各项能量基本相同;随机场时、空尺度敏感性试验发现,SPP扰动随机场时间、空间相关尺度对集合预报效果有明显影响,当扰动随机场选用12 h抗相关时间及截断波数20时,集合预报结果最优。上述结果表明,SPP随机参数扰动方案不仅能够有效提高集合概率预报效果,还能够提高集合降水概率预报技巧,具有良好的业务应用与发展前景。      

飞机积冰预报算法对比及其集成预报模型研究

以人工增雨作业获取的飞机积冰实例资料为基础,利用WRF模式对51次飞机积冰过程进行数值模拟,对比分析了常用七种积冰预报算法对积冰潜势区和强度的预报效果,进而采用评分权重集成法建立了飞机积冰强度集成预报模型,并检验了其预报效果。结果表明:(1)假霜点温度经验法对2002年4月4日积冰个例的预报效果与实况一致,而其他积冰算法预报效果均与实况相差较大;(2)对51次飞机积冰预报效果进行统计检验发现,假霜点温度经验法的预报效果最好,积冰强度预报准确率为72.55%,其次是RAOB法,IC指数法和I积冰指数法次之,改进的IC指数法预报准确率最差,只有19.61%;(3)对比不同积冰算法建立的集成预报模型的预报效果发现,选用IC指数法、假霜点温度经验法、RAOB法进行集成预报时,预报准确率最高,且漏报率、偏弱率及偏强率均能控制在10%以内,比单一预报算法中的最高预报准确率提高了8%,且漏报率降低了4%,偏强率降低了8%。     

GRAPES区域集合预报两种侧边界扰动方法对比试验

中国气象局数值预报中心自2014年建立了区域集合预报业务系统,其使用的侧边界扰动由全球集合预报系统动力降尺度得到。为深入了解侧边界扰动对区域集合预报的影响,基于15 km水平分辨率的区域集合预报模式,使用动力降尺度方法和尺度化滞后平均法(scaled lagged average forecasting,SLAF)设计构造了两种侧边界扰动方案,并开展了2015年7月共6天的集合预报试验,利用集合均方根误差、集合离散度、连续分级概率评分、离群值、Brier Score及相对作用特征曲线面积等概率预报检验方法进行了多方面检验,分析了两种侧边界扰动方案对区域集合预报质量的影响。结果表明:动力降尺度侧边界扰动方案(DOWN)的扰动总能量在各垂直层次均大于SLAF方案,使得边界上前者的离散度大于后者,集合扰动增长更为合理;对于等压面要素和地面要素,DOWN方案的离散度、Outlier、CRPS等评分优于SLAF方案,反映了DOWN方案构造的侧边界扰动更加合理;在降水概率预报技巧方面,SLAF方案在评分上具有一定优势,但评分的提高没有通过显著性水平检验,因此认为两种方案对降水预报的改进基本相当。     

A One-dimensional Ensemble Forecast and Assimilation System for Fog Prediction

A probabilistic fog forecast system was designed based on two high resolution numerical 1-D models called COBEL and PAFOG. The 1-D models are coupled to several 3-D numerical weather prediction models and thus are able to consider the effects of advection. To deal with the large uncertainty inherent to fog forecasts, a whole ensemble of 1-D runs is computed using the two different numerical models and a set of different initial conditions in combination with distinct boundary conditions. Initial conditions are obtained from variational data assimilation, which optimally combines observations with a first guess taken from operational 3-D models. The design of the ensemble scheme computes members that should fairly well represent the uncertainty of the current meteorological regime. Verification for an entire fog season reveals the importance of advection in complex terrain. The skill of 1-D fog forecasts is significantly improved if advection is considered. Thus the probabilistic forecast system has the potential to support the forecaster and therefore to provide more accurate fog forecasts.     

ALow-order Model of Two-dimensional Fluid Dynamics on the Surface of a Sphere

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SWCWARMS模式小时降水量时间滞后集合预报研究

基于SWCWARMS模式9km×9km分辨率逐小时降水预报,通过时间滞后集合预报方法构建多个集合成员,使用SAL评分值计算相应集合成员的权重系数,进行不等权集合平均,从而得到新的逐小时降水预报。利用SAL、TS和BIAS检验方法对四川省2019年8月的逐小时降水量时间滞后集合预报及相应的SWCWARMS模式最新时次预报进行对比分析,结果表明:(1)时间滞后集合预报SAL检验的L值和A值都较模式预报更接近于0,较好地改善了降水位置和强度的整体预报水平;(2)时间滞后集合预报对逐小时降水晴雨TS评分提升明显,评分提高百分率在10%左右,有效地减小了模式在晴雨方面的空报;(3)临近预报时效,时间滞后集合预报方法对于大量级降水预报也有较好的订正效果。     

Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique

ABSTRACT

The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (T_max) during summertime in a mega urban area. A total of 10 geographic variables, including the clear-sky averaged land surface temperature and the normalized difference vegetation index, were used as input variables. The stacking model was compared to Cokriging, three individual data-driven methods, and a simple average ensemble model, all through leave-one-station-out cross validation. The stacking model showed the best performance by improving the generalizability of the individual models and mitigating the sensitivity to the extreme daily T_max. This study demonstrates that the stacking ensemble method can improve the accuracy of spatial interpolation of environmental variables in various research fields.