雷电定位系统与人工观测雷暴日数统计比较

为了利用雷电定位系统 (lightning location system,LLS) 资料统计人工观测雷暴日数,采用湖北省2007—2012年LLS监测资料,选取25个气象站为圆心,统计其不同监测半径 (r) 圆区域内LLS监测的雷电日数,并与人工观测雷暴日数进行比较。结果表明:r≤7 km时,LLS监测平均年雷电日数小于人工观测平均年雷暴日数;r≥8 km时, LLS监测平均年雷电日数大于人工观测平均年雷暴日数;r=22 km圆区域内年平均雷电日数可替代最大年雷暴日数。根据r=7 km,r=8 km圆区域内LLS监测的年雷电日数、年平均地闪密度资料,分别采用直接替代法、地闪密度法和该文提出的二元法计算年雷暴日数,结果显示:二元法效果最好。二元法计算的2007—2012年25个站平均年雷暴日数与人工观测相等,平均差异为7.4%;二元法计算的2013年年雷暴日数与人工观测相差0.8 d,平均差异为12.3%。     

中国雨季的一种客观定量划分

从客观分析角度出发,利用有序样本最优分割法对中国610个台站的气候平均（1961—2010年）候降水序列进行有序分割,给出中国不同区域的雨季定量划分。根据中国13个区域候降水量的气候平均值分布特征,并基于有序样本最优分割法的划分结果需同时满足分割段内波动小、段间差异大的要求,确定了各区域的合理分割数,通过制定3种雨季划分方案,对中国区域雨季进行了细致的定量划分。第1种方案将全年降水划分为雨季和旱季,结果表明,雨、旱两季差异明显的地区出现在华南西部沿海和新疆邻近区域;第2种方案将全年降水划分为雨季相对干期、雨季相对湿期和旱季3个降水阶段,这种特征出现的区域为华南大部分地区、江南地区、长江中下游地区、西南地区东部和南部,以及西北地区中东部;第3种方案将全年降水划分为春雨季、主雨季、秋雨季和旱季,出现这种特征的区域为长三角及淮河流域、黄淮和华北地区、东北地区、西北地区中部、内蒙古地区西部、青藏高原中东部及其以东地区。与已有的中国不同区域降水特征研究结果的比较表明,有序样本最优分割法不仅对中国雨季的划分客观有效,且其划分结果合理并具有明确的气象意义。     

太阳活动周与全球大震

为研究太阳活动与全球大震的关系, 引入一个无量纲的"地震能量函数√G", 并分析研究了1681—2011故年间全球M≥7.0大震的能量释放的时间序列.由此发现全球大震在太阳活动周4个阶段的分布和活动度, 随震级的强度而异.提出地壳对太阳风暴加卸载响应模式, 用于解释此现象: 通过考察最近331 a, 得出全球共发生了10个M≥9.0超级巨震的时空分布特征, 特别是太阳活动峰年期间没有发生过超级巨震.该研究结果可为判断全球大震提供参考.      

基于VBA实现设计点位编号和坐标提取

针对实际生产需要,探讨利用计算机实现化探设计点位坐标提取和填写样品编号,重点介绍用VBA编程填写样品编号。这里还介绍了VBA语言及主要函数功能、程序流程及主要程序编码。通过实际应用表明,该程序流程合理、过程简单、运行速度快,有效改善了野外生产的精度及效率。     

Prediction of depth‐integrated fluxes of suspended sediment in the Amazon River: particle aggregation as a complicating factor

Large rivers have been previously shown to be vertically heterogeneous in terms of suspended particulate matter (SPM) concentration, as a result of sorting of suspended solids. Therefore, the spatial distribution of suspended sediments within the river section has to be known to assess the riverine sedimentary flux. Numerous studies have focused on the vertical distribution of SPM in a river channel from a theoretical or experimental perspective, but only a few were conducted so far on very large rivers. Moreover, a technique for the prediction of depth‐integrated suspended sediment fluxes in very large rivers based on sediment transport dynamics has not yet been proposed. We sampled river water along depth following several vertical profiles, at four locations on the Amazon River and its main tributaries and at two distinct water stages. Depending on the vertical profile, a one‐ to fivefold increase in SPM concentration is observed from river channel surface to bottom, which has a significant impact on the ‘depth‐averaged’ SPM concentration. For each cross section, a so‐called Rouse profile quantitatively accounts for the trend of SPM concentration increase with depth, and a representative Rouse number can be measured for each cross section. However, the prediction of this Rouse number would require the knowledge of the settling velocity of particles, which is dependent on the state of aggregation affecting particles within the river. We demonstrate that in the Amazon River, particle aggregation significantly influences the Rouse number and renders its determination impossible from grain‐size distribution data obtained in the lab. However, in each cross section, the Rouse profile obtained from the fit of the data can serve as a basis to model, at first order, the SPM concentration at any position in the river cross section. This approach, combined with acoustic Doppler current profiler (ADCP) water velocity transects, allows us to accurately estimate the depth‐integrated instantaneous sediment flux. Copyright © 2010 John Wiley & Sons, Ltd.     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulation of probability distributions commonly used in hydrological frequency analysis

Random variable simulation has been applied to many applications in hydrological modelling, flood risk analysis, environmental impact assessment, etc. However, computer codes for simulation of distributions commonly used in hydrological frequency analysis are not available in most software libraries. This paper presents a frequency‐factor‐based method for random number generation of five distributions (normal, log–normal, extreme‐value type I, Pearson type III and log‐Pearson type III) commonly used in hydrological frequency analysis. The proposed method is shown to produce random numbers of desired distributions through three means of validation: (1) graphical comparison of cumulative distribution functions (CDFs) and empirical CDFs derived from generated data; (2) properties of estimated parameters; (3) type I error of goodness‐of‐fit test. An advantage of the method is that it does not require CDF inversion, and frequency factors of the five commonly used distributions involves only the standard normal deviate. Copyright © 2006 John Wiley & Sons, Ltd.     

Multi-agent Blind Model and Its Application to Regional Eco-environmental Quality Assessment

1 Introduction Eco-environmental quality assessment is an activity that is affected by man-made factors. As other environmental systems, regional eco-environment is also a complicated and uncertain system (Wu et al., 2005). Owing to these reasons, people may produce a style of subjective un- certainty in mind in evaluating such system (Liu et al. 1999a; Wu et al., 2004). According to unascertained mathematics (Liu et al., 1997; Liu et al., 1999b), we know that this kind of subjective uncerta…