多普勒雷达观测在北京7.21暴雨数值模拟中的三维变分同化研究

基于背景误差的特征长度理论,研究调整背景误差水平分辨率对多普勒雷达资料三维变分同化的影响。首先利用NMC方法针对暴雨落区统计不同水平分辨率的背景误差协方差,分析两种不同分辨率的背景误差的结构特征,研究水平分辨率对背景误差特征长度的影响。将其应用于雷达资料同化中,研究背景误差水平分辨率变化对雷达资料同化的影响。结果表明:背景误差水平分辨率由27 km提高到3 km时,在大气低层体现出更细致的动力场信息,其动力场水平特征长度按水平分辨率的二次根递减,而温度场与水汽场水平特征长度变化不明显。在将不同分辨率的背景误差用于三维变分同化时,更高分辨率的背景误差可以在分析场增量中体现更细致的中小尺度信息,能够明显改善雷达径向速度资料同化效果,并在随后的暴雨数值模拟中雨量及其分布形态更接近实况。     

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%。     

二维滞弹性非静力平衡云模式第二部分──中低空风切变对云微物理过程的影响

本文运用已建立的二维时变云模式［1］，就中低空风垂直切变对对流和云微物理过程的影响进行了模拟研究。为此设计了五种方案进行了对比试验。结果表明：中低空风切变较小时，容易爆发对流；中低空切变不超过某一值时，对流发展缓慢，但最终会爆发强对流；一旦中低空风切变超过某一值时，则对流发展不起来。另一方面，中低空风切变可加强各云微物理过程及地面降水量。     

GRAPES全球模式的动能谱分析

大量的观测资料表明在对流层高层和平流层低层,大气动能谱在大尺度范围(800 km以上)与波数呈现出-3的斜率关系,而过渡到中尺度范围(400 km以下)二者呈现出-5/3的斜率关系。模式预报结果能否展现大气动能谱的这种斜率转折特征已经成为模式评估的有效方法。本文采用GRAPES全球中期数值预报模式2013年5月的预报结果,计算了模式一维和二维动能谱,并分析了二维谱的旋转分量与辐散分量、平均分量与扰动分量的关系。结果表明,GRAPES模式的一维谱和二维谱结果均能较好地表现出动能谱的斜率转折特征,但该模式动能谱在中尺度范围的斜率绝对值明显比观测结果和其它模式结果偏大;二维谱的旋转分量和辐散分量、平均分量和扰动分量在总动能谱中的比重关系及其随高度的变化均与文献结果较为一致。总之,GRAPES模式整体上较好地再现了大气动能谱的各种特征,但是可能由于模式分辨率和耗散作用的影响,模式对大气中尺度波动的描述还不够充分。     

西宁曹家堡机场雷暴统计特征分析

基于2006—2015年西宁曹家堡机场的雷暴观测资料,从雷暴日（次）数、持续时间、初现方位、伴随天气现象和雷暴初终日特征方面对该高原机场的雷暴特征进行统计分析。结果表明,曹家堡机场年平均雷暴日数为34.7 d,主要发生在5—9月,夏季是该机场雷暴的集中爆发期;雷暴具有明显的日变化特征,集中发生在午后至夜间（16—22时）,持续时间在2 h以内;雷暴初现方位以偏西、偏北为主,偏西、偏北的对流云团发展是该机场飞行保障的关注重点;雷暴伴随天气现象以干雷暴和小阵雨为主,而少数伴有中、大阵雨的强雷暴主要发生在夏季;雷暴初终日平均间隔为173.2 d,并且初雷能充分体现该机场的典型雷暴特征。     

亚洲季风区不同形态中尺度对流系统的特征分析

利用16 a的TRMM卫星观测资料,分析了亚洲季风区准圆状、线状和拉长状中尺度对流系统(MCSs)的空间分布、对流属性及其区域变化特征。结果表明:拉长状MCSs的数量最多,准圆状的其次而线状的最少。自西向东,准圆状(线状)MCSs数量占各区域MCSs总数的比例逐渐减小(增加),线状MCSs在副热带和洋面地区的产生几率相比更大。MCSs的发生频次呈现以暖季(5—9月)为峰值的单峰分布,准圆状和拉长状MCSs的暖季峰值比线状MCSs的大。MCSs主要发生在下午—傍晚时段,但线状MCSs在午夜—凌晨出现的概率比其它两种大。3种类型MCSs的整体强度基本表现为副热带地区弱于近热带地区,但不同类型MCSs的强度差异在各区域不尽相同,如中国中东部地区和西北太平洋地区的准圆状MCSs强度最强,但东海及其以东洋面的线状MCSs强度最强。     

A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China

A three-dimensional variational(3DVAR) data assimilation(DA) system is presented here based on a size-resolved sectional aerosol model, the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC) within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem) model. The use of this approach means that both gaseous pollutants such as SO_2, NO_2, CO, and O_3 as well as particulate matter(PM_(2.5), PM_(10)) observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted, one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC) and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts. Results show that, in the first place, use of the DA system can provide a more accurate model initial field. The root-mean-square error of PM_(2.5), PM_(10), SO_2, NO_2, CO, and O_3 mass concentrations in analysis field fell by 29.27 μg m~(-3)(53.5%), 34.5 μg m~(-3)(50.9%),30.36 μg m~(-3)(64.2%), 8.91 μg m~(-3)(39.5%), 0.46 mg m~(-3)(47.4%), and 15.11 μg m~(-3)(51.0%), respectively, compared to a background field without assimilation. At the same time, mean fraction error was reduced by 42.6%, 53.1%, 45.2%, 43.1%,69.9%, and 48.8%, respectively, while the correlation coefficient increased by 0.51, 0.55, 0.48, 0.38, 0.47, 0.65, respectively.Secondly, the results of this analysis reveal variable benefits from assimilation on different pollutants. DA significantly improves PM_(2.5), PM_(10), and CO forecasts leading to positive effects that last more than 48 h. The positive effects of DA on SO_2 and O_3 forecasts last up to 8 h but that remains relatively poor for NO_2 forecasts. Thirdly, the influence of assimilation varies in different areas. It is possible that the positive effects of DA on PM_(2.5) and PM_(10) forecasts can last more than 48 h across most regions of China. Indeed, DA significantly improves SO_2 forecasts within 48 h over north China, and much longer CO assimilation benefits(48 h) are found in most regions apart from north and east China and across the Sichuan Basin. DA is able to improve O_3 forecasts within 48 h across China with the exception of southwest and northwest regions and the O_3 DA benefits in southern China are more evident, while from a spatial distribution perspective, NO_2 DA benefits remain relatively poor.     

Application of Gaussian Weight to Improve Perturbation Features of Convection-Permitting Ensemble Forecast Based on Local Breeding of Growing Modes

Local breeding of growing modes(LBGM) is a method used to generate initial condition perturbation(ICP) for convection-permitting ensemble forecasts. Equal weights(EWs) are usually presumed in LBGM during the localization of ICP, without considering different contributions of the grid points within the local radius. To address this problem, Gaussian weights(GWs) are proposed in this study, which can accommodate the varied influences of the grids inside the local radius on the central grid through a Gaussian function. Specifically, two convection-permitting ensemble forecast experiments based on LBGM with GWs and EWs are compared and analyzed respectively for two squall line cases. The results showed that the use of the GWs intensified the local characteristics of the ICP and made the distribution of the ICP fields more flow-dependent. Kinetic energy spectrum of the ICP indicated that there could be more large-scale information in the ICP by using the GWs. In addition, mesoscale information also improved slightly. For forecast of nonprecipitation variables, GWs improved the relationship between the root-mean-square error and the spread and contributed to the forecasting accuracy of wind, temperature, geopotential height, and humidity. For the precipitation forecast, GWs simulated the precipitation structure successfully and provided better probability forecasting during the evolution of the two squall line processes than the EWs.     

暴雨模拟中多普勒雷达径向速度变分同化的应用

针对2008年6月广东地区的一次强降雨过程,利用WRF中尺度数值模式及其三维变分同化系统(WRF-3DVAR),进行了多普勒雷达径向速度变分同化对暴雨过程模拟效果影响研究。结果表明:WRF-3DVAR能够有效地同化多普勒雷达径向速度,同化后的主要影响在于改进了初始动力场,使得初始场包含有更详尽的中尺度特征信息,进而显著提高模式对广东局地暴雨过程的模拟效果。在高分辨率中尺度数值模式中有效地利用多普勒天气雷达资料,是提高中尺度降雨预报的关键。     

基于时空不确定性的对流尺度集合预报效果评估检验

针对对流尺度天气系统的高度非线性特征和高分辨率模式预报结果存在时、空不确定性现象,以及当前邻域概率法主要考虑高分辨率预报结果的空间位移误差,而不能有效解决预报结果存在时间超前与滞后问题,将时间因素引入到邻域概率法中,结合一次强飑线过程进行对流尺度集合预报试验,并基于改进后的新型邻域概率法与分数技巧评分,对降水预报进行了不同时、空尺度的效果评估检验。结果表明:（1）邻域集合概率法和概率匹配平均法在极端降水的分数技巧评分远高于传统集合平均,弥补了集合平均对极端降水预报能力偏低的缺陷。（2）对于此类飑线过程的对流尺度天气系统而言,邻域半径为15—45 km的空间尺度能够改善降水位移误差的空间不确定性,并使其预报效果达到最优,其中15—30 km的邻域半径对于尺度更小的大量级降水事件预报能力更强。（3）对流尺度降水预报考虑时间尺度与降水强度存在着对应关系,不同时间尺度可以捕获到不同量级降水的时间不确定性。同时,时间尺度与空间尺度对于降水预报效果的影响是相互关联的。（4）改进的邻域概率法能够同时体现高分辨率模式预报结果在对流尺度降水事件上存在的时、空不确定性,实现了对流尺度降水在时、空尺度上的综合评估,并能为不同量级降水提供与其时、空尺度相匹配的概率预报结果。