不同闪电跃增算法在北京地区应用效果对比

基于S波段多普勒天气雷达基数据、北京闪电定位网全闪定位数据和北京地区降雹的人工观测结果,对比分析Gatlin和σ两种闪电跃增算法在不同配置下对北京地区2015—2018年共177次冰雹天气过程的预警效果。结果表明：不同倍数的σ算法预警结果差别很大,2σ（要求当前闪电频数变化率超过之前平均闪电频数变化率两倍标准差）在σ算法中的预警效果最佳;不同N（总闪频数变化率的数量）配置下的Gatlin算法的预警结果差别不大,其中当N=6时的预警效果最佳。2σ算法的命中率、虚警率和临界成功指数分别为80.2%,41.6%和51.1%,N=6的Gatlin算法的相应结果分别为82.5%,62.0%和35.2%。另外,详细分析了一次多单体雷暴过程和一次飑线过程中两种算法的应用情况,结果也表明Gatlin算法比2σ算法的命中率略高,但虚警率偏高很多,临界成功指数偏低。综合Gatlin算法和σ算法对冰雹预报结果评估情况,发现2σ闪电跃增算法更适于对北京冰雹天气的预警,对提升闪电数据在北京地区冰雹预报业务的可用度有一定参考价值。     

隆林县石漠化地区发展金银花种植气候适应性分析

以隆林县石漠化地区的气候条件为依据,采用比较分析方法,对金银花的气候生态规律和环境因子对金银花生长、产量的影响进行分析,找出其影响的气候指标。结果表明,隆林县石漠化地区金银花生长条件较好,西北部山区和南部高寒山区为适宜区,其它区域仅为次适宜区,为石漠化地区发展金银花种植提供气候依据。     

济南市一次持续大雾过程分析

利用常规气象观测资料对2007年12月20-23日济南市一次持续大雾天气过程的大尺度天气背景、地面气压场、温度对数压力图以及单站气象要素进行了分析,结果表明:雾是在稳定的大气层结下出现的天气现象,根据大气稳定层结的状况及变化,可以判断大雾天气的有无及生消时间;近地面逆温和低空逆温同时存在,表明大气层结非常稳定,近地面逆温有利于近地面层水汽积累,低空逆温使近地面层水汽不易扩散而聚集,有利于近地面层维持潮湿,持续几天出现大雾;连续几天大气层结都处于稳定状态,地面风力很小,这些都限制了近地面层的水汽向外耗散,底层相对湿度较大,为大雾的形成提供了充沛的水汽条件.     

祁连山空中云水资源开发潜力研究新进展

祁连山及河西走廊地区是我国气候变化敏感区和生态脆弱区。本文通过对2005a以来祁连山云水资源相关最新研究成果的总结,旨在揭示气候变暖对祁连山云水资源影响包括气候变化、水汽时空变化、地形云特征的基础上,分析评估了祁连山云水资源开发潜力及效益。实验研究表明,在祁连山区开展人工增雨(雪)作业,每年可增加降水量3.7×10_⁸～７.4×10_⁸m_³。近10年来,甘肃春季飞机人工增雨作业年均增加降水量为13.5×10_⁸m_³,平均投入产出比为1:30。     

风云三号降水测量雷达技术性能分析

风云三号降水测量卫星主要用于台风等灾害性天气系统强降水的监测,并提供全球中低纬度地区降水的结构信息。降水三维结构的准确测量是通过风云三号降水测量卫星的核心载荷——双频降水测量雷达实现的。首先介绍了风云三号降水测量雷达的主要任务与探测能力要求,然后通过仿真技术对降水测量雷达距离分辨率、水平分辨率、扫描角度、天线旁瓣电平、测量精度、波束匹配精度等主要技术性能指标进行了详细的分析。最后,利用于2010年开展的降水测量雷达样机航空校飞试验评估验证了降水测量雷达的各项功能和主要性能参数。结果表明,风云三号降水测量雷达整体降水探测性能与全球降水测量卫星的双频降水雷达相当。     

南昌市城区暴雨积水的数值模拟

论述了南昌市城市暴雨积水仿真系统的数学原理和开发成果,并应用实况降水对该系统的模拟精度进行测试。结果表明:大多数(62.6%)模拟计算结果的绝对误差在10 cm以内,只有极少数(2.4%)实际积水与模拟结果的误差超过30 cm。暴雨积水等级试验结果表明,中度以上暴雨积水地段的预报准确率达98%,轻度积水和无积水地段的预报准确率达92%。总体来看,暴雨积水趋势(等级)预报基本准确,定量(积水深度)预报有误差,平均相对误差为6%,模型的预测结果与实况基本相符。通过人工给定不同强度的雨量来模拟南昌市两个重点积水地段的积水过程,得到结论:当降水强度达到20 mm/h时,开始产生积水,降水强度超过30 mm/h时将产生严重积水;两个积水点因排水条件不一样,退水时间差异较大。排水条件差的地段,中—大雨需要15 h退完,暴雨需要24 h以上才能退完;在不同降水强度和排水条件下,最大积水深度出现的时间有明显区别;在暴雨情况下,绝大部分(76%)积水点的最大积水深度出现在2~3 h内。此外,讨论了模拟误差产生的原因。     

The Characteristics of Atmospheric Ice Nuclei Measured at Different Altitudes in the Huangshan Mountains in Southeast China简

The concentration of ice nuclei （IN） and the relationship with aerosol particles were measured and analyzed using three 5-L mixing cloud chambers and a static diffusion cloud chamber at three altitudes in the Huangshan Mountains in Southeast China from May to September 2011.The results showed that the mean total number concentration of IN on the highest peak of the Huangshan Mountains at an activation temperature （Ta） of-20℃C was 16.6 L-1.When the supersaturation with respect to water （Sw） and with respect to ice （Si） were set to 5％,the average number concentrations of IN measured at an activation temperature of-20℃C by the static diffusion cloud chamber were 0.89 and 0.105 L-1,respectively.A comparison of the concentrations of IN at three different altitudes showed that the concentration of IN at the foot of the mountains was higher than at the peak.A further calculation of the correlation between IN and the concentrations of aerosol particles of different size ranges showed that the IN concentration was well correlated with the concentration of aerosol particles in the size range of 1.2-20 μtm.It was also found that the IN concentration varied with meteorological conditions,such as wind speed,with higher IN concentrations often observed on days with strong wind.An analysis of the backward trajectories of air masses showed that low IN concentrations were often related to air masses travelling along southwest pathways,while higher IN concentrations were usually related to those transported along northeast pathways.     

植被内部及其上方湍流场的数值模拟

植被内部及其上方的湍流流场对于了解植被与大气之间的动量、热量和质量交换过程极其重要。本文把高阶湍流封闭模型的Reynolds应力方程模型(RSM)应用于植被湍流的计算,得到了风速、湍流动能、Reynolds应力及能量耗散率的垂直分布,与现场观测数据比较,甚为满意。     

Characterizing uncertainty in geotechnical design of energy piles based on Bayesian theorem

It is known that a lot of uncertainties are involved in geotechnical design of energy piles. In this paper, a Bayesian updating framework is presented to characterize those uncertainties. The load-transfer model is developed to predict the thermomechanical response of energy piles. Considering the cross-case variability of the uncertainty in the axial strains of pile, the global model bias is firstly calibrated by establishing a comprehensive database consisting of 12 energy pile cases. Furthermore, the uncertainty in input parameters is considered in the Bayesian updating of model bias in a specific case. The variability of the uncertain parameters is effectively reduced after updating. The coefficient of variation of prediction is decreased from 0.34 to 0.13. The present framework can well quantify uncertain factors and improve the accuracy and reliability of the prediction model.