Multivariate nonparametric resampling scheme for generation of daily weather variables

A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.     

沈阳地区沙尘天气分析

本文总结了沈阳地区沙尘天气的时空分布特点，从天气条件、地表自然条件(沙源)两方面分析了沈阳地区沙尘天气的成因，总结了沈阳地区预报沙尘天气时应参考的5个因素。     

Vertical dynamic and thermodynamic characteristics of urban lower boundary layer and its relationship with aerosol concentration over Beijing

By utilizing observational data from a 325 m tower of the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) on March 19-29, 2001 and August 11-25, 2003, a comprehensive study was conducted on the vertical dynamical and thermodynamic characteristics of the urban lower boundary layer (ULBL) and its relationship with aerosol concentration over Beijing. Firstly, a comparative analysis was made on the gradient data (wind, temperature and humidity), ultrasonic data (atmospheric turbulences) and air-quality observations at different tower heights (47, 120 and 280 m). Secondly, a diagnosis was made to reveal the major features of normalized variances of velocity and temperature, turbulence kinetic energy as well as their relationship with aerosol concentrations. Furthermore, the characteristics of the ULBL vertical structure and the TSP concentration/distribution variations during a sand/dust weather process were also analyzed. The outcome of the study showed that under unstable stratification, the normalized variances of velocity (σu/u*, σv/u*, σw/u*) and temperature (σT/T*) at 47 and 120 m heights fit the Monin-Obukhov similarity (MOS) framework and the fitting formulas were given out accordingly. According to the stratification parameter (z′/L), the stable ULBL could be divided into 2 zones. With z′/L<0.1, it was a weakly stable zone and MOS framework was applicable. The other was a highly stable zone with z′/L>0.1 and the normalized velocity variances tended to increase along with higher stability, but it remained constant for normalized temperature variances. At daytime, the near-surface layer includes two heights of 47 and 120 m, while 280 m has been above it. The ULBL analysis in conjunction with a sand/dust weather process in Beijing in March 2001 indicated that the maximum concentration of Total Suspended Particulates (TSP) at 320 m reached 913.3 μg/m3 and the particles were transported from the upper to lower ULBL, which was apparently related to the development process of a low-level jet and its concomitant strong sinking motion.     

贵州省地质灾害气象预警预报

贵州复杂和脆弱的地质环境使地质灾害频发,危害民生,影响国民经济的健康发展,引起社会的重视。选用适合的地质灾害气象预警预报方法,是主动防灾减灾的手段之一。几年来的预警预报工作证明,效果总体较好。对其重要性、影响、作用、改进的意义做了解读。     

2008年“7.02”滇中大暴雨的成因诊断与数值模拟

利用常规观测、NCEP 1°×1°再分析资料、云图、多普勒雷达回波和WRF模式对2008年7月2日滇中大暴雨进行成因诊断和数值模拟。结果表明:对流层高层的干侵入和中低层冷、暖平流交汇诱发副热带高压和滇缅高压间辐合低涡迅猛发展成强中尺度对流辐合体,加上中低层来自孟加拉湾的丰富水汽输送和中低层强水汽辐合共同引发此次大暴雨。过程中,垂直螺旋度贡献主要在中层;干位涡呈现出对流层顶强正高位涡,300 hPa以下为次正高位涡,两者之间为负区的柱状分布特征,次正高位涡强中心有向下层延伸特征。WRF较好地模拟了整个大暴雨过程中强降水主体时段和大暴雨落区特点,最大对流有效位能变化趋势对强降水有较好预示作用,模拟方案在积分30小时内效果较好。     

相控阵天气雷达监测预警龙卷风的过程剖析

天气雷达作为龙卷风监测预警的重要手段之一,应用具有超高时空分辨率的X波段双极化相控阵天气雷达系统,较好地捕获并提前预警龙卷风。以2022年6月19日07时发生在广东佛山南海的一次龙卷风为例,详细剖析龙卷生消及雷达监测预警过程。借助雷达智能预警软件,利用X波段双极化相控阵天气雷达的双偏振量和超高时空分辨率数据,实时反演三维风场和分析龙卷碎片(TVS)特征,能够显著提高龙卷风监测预警水平。实例表明,本次成功地提前18分钟预警龙卷,进一步说明了X波段双极化相控阵天气雷达在强对流天气探测方面具有较强的生命力。     

松花江、嫩江流域1998年夏季暴雨过程天气分析

利用松花江、嫩江流域内９４个气象台站的观测资料和ＮＣＡＲ／ＮＣＥＰ再分析资料,分析了１９９８年夏季降水集中期和主要暴雨过程发生时的天气形势特点。之所以会连续出现暴雨、大暴雨,归纳起来有以下天气特点：（１）亚洲中高纬度阻塞形势稳定;（２）长时间受东北冷涡控制;（３）西太平洋副热带高压短时间北进,位置适中;（４）盛夏北方季风较强盛。     

江淮地区冬季一次罕见强对流风暴的数值模拟结果诊断分析

本文利用数值模拟的诊断分析结果对出现在江苏地区冬季的一次强对流风暴的成因进行了研究,结果表明,在河套气旋缓慢东移过程中,冷锋前暖区较长时期的大雾天气及下湿上干的湿度分布为强风暴的产生准备了大气层结不稳定的条件,中尺度辐合流场对强风暴的产生起了触发作用。对流云的下沉辐散气流是雷暴向前传播的重要机制。     

邕宁夏季高温天气分析

对邕宁2004年夏季一次高温天气过程进行了阐述,对高温天气发生的原因进行了初步分析,结果发现,稳定的副热带高压与热带风暴共同影响是本次高温过程的主要特点,持续稳定的下沉气流,也是造成高温天气的另一重要原因。     

A Comparison of Four Precipitation Distribution Models Used in Daily Stochastic Models

Stochastic weather generators are statistical models that produce random numbers that resemble the observed weather data on which they have been fitted; they are widely used in meteorological and hydrologi- cal simulations. For modeling daily precipitation in weather generators, first-order Markov chain-dependent exponential, gamma, mixed-exponential, and lognormal distributions can be used. To examine the perfor- mance of these four distributions for precipitation simulation, they were fitted to observed data collected at 10 stations in the watershed of Yishu River. The parameters of these models were estimated using a maximum-likelihood technique performed using genetic algorithms. Parameters for each calendar month and the Fourier series describing parameters for the whole year were estimated separately. Bayesian infor- mation criterion, simulated monthly mean, maximum daily value, and variance were tested and compared to evaluate the fitness and performance of these models. The results indicate that the lognormal and mixed-exponential distributions give smaller BICs, but their stochastic simulations have overestimation and underestimation respectively, while the gamma and exponential distributions give larger BICs, but their stochastic simulations produced monthly mean precipitation very well. When these distributions were fitted using Fourier series, they all underestimated the above statistics for the months of June, July and August.