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In consideration of large uncertainties in severe convective weather forecast, ensemble forecasting is a dynamic method developed to quantitatively estimate forecast uncertainty. Based on ensemble output, joint probability is a post-processing method to delineate key areas where weather event may actually occur by taking account of the uncertainty of several important physical parameters. An investigation of the environments of little rainfall convection and strong rainfall convection from April to September (warm season) during 2009-2015 was presented using daily disastrous weather data, precipitation data of 80 stations in Anhui province and NCEP Final Analysis (FNL) data. Through ingredients-based forecasting methodology and statistical analysis,four convective parameters characterizing two types of convection were obtained, respectively, which were used to establish joint probability forecasting together with their corresponding thresholds. Using the ECMWF ensemble forecast and observations from April to September during 2016-2017, systematic verification mainly based on ROC and case study of different weather processes were conducted. The results demonstrate that joint probability method is capable of discriminating little rainfall convection and non-convection with comparable performance for different lead times, which is more favorable to identifying the occurrence of strong rainfall convection. The joint probability of little rainfall convection is a good indication for the occurrence of regional or local convection, but may produce some false alarms. The joint probability of strong rainfall convection is good at indicating regional concentrated short-term heavy precipitation as well as local heavy rainfall. There are also individual missing reports in this method, and in practice, 10% can be roughly used as joint probability threshold to achieve relative high TS score. Overall, ensemble-based joint probability method can provide practical short-term probabilistic guidance for severe convective weather. 相似文献
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Many popular groundwater modeling codes are based on the finite differences or finite volume method for orthogonal grids. In cases of complex subsurface geometries this type of grid either leads to coarse geometric representations or to extremely fine meshes. We use a coordinate transformation method (CTM) to circumvent this shortcoming. In computational fluid dynamics (CFD), this method has been applied successfully to the general Navier–Stokes equation. The method is based on tensor analysis and performs a transformation of a curvilinear into a rectangular unit grid, on which a modified formulation of the differential equations is applied. Therefore, it is not necessary to reformulate the code in total. We applied the CTM to an existing three-dimensional code (SHEMAT), a simulator for heat conduction and advection in porous media. The finite volume discretization scheme for the non-orthogonal, structured, hexahedral grid leads to a 19-point stencil and a correspondingly banded system matrix. The implementation is straightforward and it is possible to use some existing routines without modification. The accuracy of the modified code is demonstrated for single phase flow on a two-dimensional analytical solution for flow and heat transport. Additionally, a simple case of potential flow is shown for a two-dimensional grid which is increasingly deformed. The result reveals that the corresponding error increases only slightly. Finally, a thermal free-convection benchmark is discussed. The result shows, that the solution obtained with the new code is in good agreement with the ones obtained by other codes. 相似文献
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对2009年7月至2010年6月区域空气质量数值预报模式CAPPS3在福州市的应用进行效果检验,分析各季节CAPPS3预报福州市3种污染物SO2、NO2和PM10的等级预报准确率、转折性天气预报准确率与监测值的相关系数以及综合评分。结果表明:夏秋季节CAPPS3模式3种污染物等级预报准确率较高,冬季NO2和春季PM10的等级预报准确率较低,错误等级预报多数偏高;转折性天气预报准确率夏秋季最高,春季最低,模式对天气形势的变化反应不灵敏,特别是污染物浓度突变时,预报能力较差,当天气形势稳定时,预报效果较好;浓度预报值较监测值有偏大和滞后的缺点;相关系数及综合评分结果夏秋季最高,春季最低。CAPPS3总体预报效果较好,可提供有价值的指导预报,适合业务运行。 相似文献
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为提高降水相态预报准确率,以北京南郊观象台为代表站,选取1951年1月至2011年4月逐日20时至20时出现的雨夹雪日数,统计分析其月变化特征;再选取2000-2011年10月-4月08时和20时出现的雨、雨夹雪、雪个例,使用与降水个例对应时次的地面及高空常规观测数据,统计分析各层常用气象要素在各降水相态判别中的作用,并找出主要影响因子及降水相态判别指标;应用多元回归方法,建立降水相态统计预报方程,并对方程及判别指标进行检验。结果表明:雨和雪的温度、露点平均值在低层(地面到850 hPa)有明显差异,但随着高度增加其差异逐渐减小;因雨和雪的温度露点差平均值各层差异均很小,使用温度露点差很难区分降水相态;经过对2011年11月至2012年3月出现的降水相态进行检验可知,总结出的地面温度和露点判别指标以及降水相态统计预报方程均有一定的参考价值,可为今后做降水相态预报服务提供参考依据。 相似文献
36.
2006年T213模式在江苏的降水和温度检验评估 总被引:4,自引:1,他引:4
为了了解T213模式在江苏区域的预报性能,对该模式在江苏区域的降水和温度预报进行了检验和评估.检验结果表明,在江苏区域,T213模式晴雨预报正确率近70%,模式对降水预报具有一定的指示作用,影响中小量级降水TS评分的主要因素是模式预报的虚假降水成分较多,中等以上量级则存在降水中心落区预报偏差较大的情况.T213模式温度预报值系统性偏低3 ℃左右,模式分析场的温度偏低且与实况误差较大,是造成这种情况的主要原因,因此进行温度订正是必要的. 相似文献
37.
Large-eddy simulation of turbulent flow past a circular cylinder at sub- to super-critical Reynolds numbers is performed using a high-fidelity orthogonal curvilinear grid solver. Verification studies investigate the effects of grid resolution, aspect ratio and convection scheme. Monotonic convergence is achieved in grid convergence studies. Validation studies use all available experimental benchmark data. Although the grids are relatively large and fine enough for sufficiently resolved turbulence near the cylinder, the grid uncertainties are large indicating the need for even finer grids. Large aspect ratio is required for sub-critical Reynolds number cases, whereas small aspect ratio is sufficient for critical and super-critical Reynolds number cases. All the experimental trends were predicted with reasonable accuracy, in consideration the large facility bias, age of most of the data, and differences between experimental and computational setup in particular free stream turbulence and roughness. The largest errors were for under prediction of turbulence separation. 相似文献
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对2009年7月至2010年6月区域空气质量数值预报模式CAPPS3在福州市的应用进行效果检验,分析各季节CAPPS3预报福州市3种污染物SO2、NO2和PM10的等级预报准确率、转折性天气预报准确率及与监测值的相关系数以及综合评分。结果表明:夏秋季节CAPPS3模式3种污染物等级预报准确率较高,冬季NO2和春季PM10的等级预报准确率较低,错误等级预报多数偏高;转折性天气预报准确率夏秋季最高,春季最低,模式对天气形势的变化反应不灵敏,特别是污染物浓度突变时,预报能力较差,当天气形势稳定时,预报效果较好;CAPPS3模式浓度预报值较监测值有偏大和滞后的缺点;相关系数及综合评分结果夏秋季最高,春季最低。CAPPS3总体预报效果较好,可提供有价值的指导预报,适合业务运行。 相似文献
40.
为提高数值预报模式在山东汛期降水的预报能力,为降水预报及模式物理参数方案选择和调整提供客观依据,对2010年汛期(6-9月)山东区域MM5、WRF-RUC(WRF快速循环同化系统)和T639模式24 h、48 h累积降水预报产品,进行晴雨、一般性降水和分量级降水TS评分及平均绝对误差、平均误差分析。结果表明:从晴雨预报准确率来看,3种模式相差不大;一般性降水和小雨预报,MM5模式评分结果最差,T639模式预报效果最好;中雨以上量级,24 h降水T639模式预报效果最好,特别是24 h大暴雨评分T639模式达到了10.37 %,而48 h降水T639模式预报效果下降明显。无论24 h降水还是48 h降水,除9月WRF-RUC模式平均绝对误差最小外,6-8月T639模式平均绝对误差均为最低,WRF-RUC模式24 h和48 h降水各月平均误差均为负偏差;不同的降水预报检验方案和天气过程类型对检验结果有一定的影响。 相似文献