Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.     

雷达反射率因子在中尺度云分辨模式初始化中的应用Ⅱ：数值模拟试验

将反演的云微物理量和垂直速度采用"牛顿连续松弛逼近"(Nudging)技术应用到GRAPES模式中,对一次梅雨锋暴雨过程进行了数值模拟。通过设计不同的试验方案,分别对水汽、液态水和垂直速度对Nudging效果以及预报结果的影响进行了考察。研究发现,采用Nudging初始化方法,可使背景场与观测反演资料相协调,实现了模式中对流的热启动,模式预报的开始时刻就产生降水,缩短Spin-up时间。水汽对降水至关重要,对降水的强度和持续时间都有重要影响;云水、雨水和垂直速度决定了初始时刻对流的强弱分布并产生降水;水平风场决定了系统的移动方向,对预报降水的落区有重要影响。模式比较成功地模拟了6 h的降水过程,中尺度天气系统的时空特征比较明显,对流中心上升速度最大值约2.0 m/s,云水含量400 hPa以上较大,最大值约1.5 g/kg,雨水含量500 hPa以下较大,最大约3.0 g/kg,底层辐合高层辐散。反演资料对降水的影响随预报时间的增加而减弱,预报1 h之内反演资料有明显影响,3 h之后的预报则主要依赖模式自身。鉴于仅使用一部雷达资料的反射率因子资料,雷达资料没有覆盖整个模式区域,天气系统被截断,反演和同化过程还采用了一些经验参数方法等原因,数值模拟结果与雷达观测之间还存在一定的差异,有待于更深入的研究。     

上海市电力负荷与气象因子关系及精细化预报

电力负荷预测是电力调度的重要参考依据,气象条件对电力负荷有重要影响。利用上海市2004—2008年15 min 1次的电力负荷资料和宝山气象站 (区站号58362) 同一时段3 h 1次的气象观测资料,对上海市电力负荷特征进行分析,发现不同温度区间和不同天气类型电力负荷的日变化特征有明显差别。采用逐步回归方法得到各温度区间日平均气象负荷率的预报方程,通过系数订正,得出不同天气类型的逐时负荷预报。2009年试报结果显示:采用预报日前3 d的趋势负荷平均值作为预报当日的趋势负荷值,得到的日平均负荷预测值的误差最小,约为3.6%;非工作日日平均负荷的平均预报相对误差比工作日大;日平均气温超过18 ℃时,工作日日平均负荷的预报误差较小;低于18 ℃时,预报误差明显增大;逐时负荷平均预报误差约为4%。