夏季青藏高原地区降水和低涡的数值预报试验

本文首先分析了１９７９年６—７月的ＦＧＧＥIIIｂ级资料风场和相对湿度场在青藏高原地区的偏差，指出在高原西部应予以订正。然后利用一有限区域模式，通过综合订正初始风场和相对湿度场，改进模式部分物理过程，并提高其水平分辨率，共设计了６组预报试验，对该年的两例高原低涡切变线降水过程进行了２４小时预报。结果表明，利用改进了的初始场和部分物理过程，可明显改善高原地区的降水预报，并在一定程度上改善了流场的预报，即上述改进方案是可行的；但在高原地区嵌套预报方案尚待修改，还应继续努力提高模式对高原低涡流场的预报能力。     

中-吉-乌铁路(中国段)设计洪水的研究

根据中—吉—乌铁路(中国段)沿线近10个水文观测站40多年(建站一2000年)最大洪峰流量资料，对中—吉—乌铁路(中国段)最大洪峰流量设计值进行系统的研究。应用统计学导出的P—Ⅲ分布，预测了中—吉—乌铁路(中国段)最大洪峰流量不同概率设计值。这对于中—吉—乌铁路(中国段)工程设计和施工及未来铁路养护等具有重要的科学意义和工程价值。     

Robustness of the holistic seismic risk evaluation in urban centers using the USRi

The Urban Seismic Risk index (USRi) published in a previous article (Carreño et al., Nat Hazards 40:137–172, 2007) is a composite indicator that measures risk from an integrated perspective and guides decision-making for identifying the main interdisciplinary factors of vulnerability to be reduced or intervened. The first step of the method is the evaluation of the potential physical damage (hard approach) as a result of the convolution of the seismic hazard with the physical vulnerability of buildings and infrastructure. Subsequently, a set of social context conditions that aggravate the physical effects is also considered (soft approach). According to this procedure, the physical risk index is evaluated for each unit of analysis from existing loss scenarios, whereas the total risk index is obtained by multiplying the former index by an impact factor using an aggravating coefficient, based on variables associated with the socio-economic conditions of each unit of analysis. The USRi has been developed using the underlying holistic and multi-hazard approach of the Urban Risk Index framework proposed for the evaluation of disaster risk in different megacities worldwide. This article presents the sensitivity analysis of the index to different parameters such as input data, weights and transformation functions used for the scaling or normalization of variables. This analysis has been performed using the Monte Carlo simulation to validate the robustness of this composite indicator, understanding as robustness how the cities maintain the ranking as well as predefined risk level ranges, when compared with the deterministic results of risk. Results are shown for different cities of the world.     

地震勘探中检波器组合之组内距选择方法探讨

使用相关分析方法可以定量地确定地震勘探中检波器组合的组内距，达到更有效地压制各种随机干扰，从而提高地震记录的信噪比的目的，作者在本文中，利用相关分析的思路，给出了用计算机干扰相关半径定量确定组内距的野外工作方法和数据计算方法，在实施地震施工表明，使用本方法可有效地压制各种随机干扰，达到提高地震记录信噪比的目的。     

生物土壤结皮对风沙土和黄绵土膨胀特性的影响

作为重要的土壤物理性质,膨胀性在影响土壤导水性、持水性、抗蚀性以及土壤结构的形成和发育等方面发挥着重要作用。为了探讨生物土壤结皮（BSCs）土壤的膨胀特性及其主要影响因素,针对黄土高原风沙土和黄绵土两种典型土壤,利用膨胀仪测定并比较了有、无藓结皮及其在不同因素（初始含水量、干湿循环、冻融循环、温度）下膨胀率的差异,分析了BSCs对土壤膨胀性的影响及其与环境因素和BSCs性质的关系。结果显示：风沙土上藓结皮的膨胀率为1.93%,较无结皮增加了8.65倍;而黄绵土上藓结皮的膨胀率为2.05%,与无结皮相比降低了76.68%。藓结皮的生物量和厚度与其膨胀率在风沙土上均呈线性正相关关系（P < 0.05）,在黄绵土上分别呈二次函数（P=0.02）和线性正相关关系（P=0.02）。初始含水量同时影响了土壤最大膨胀率和稳定膨胀时间,影响程度风沙土远大于黄绵土（包括藓结皮和无结皮）;干湿循环次数对无结皮土壤膨胀率的影响程度大于藓结皮土壤,其中风沙土和黄绵土上无结皮的膨胀率分别是50.00%~620.00%和-2.28%~10.81%,而两种土壤上藓结皮的膨胀率分别是-5.70%~10.88%和-10.24%~-21.46%;冻融循环下4种土壤的膨胀率均有不同程度的降低,降幅为0~18.54%。黄绵土无结皮的膨胀率受温度影响程度较大,50℃下黄绵土无结皮的膨胀率分别是25℃和35℃下的1.17倍和1.21倍。BSCs显著地改变了风沙土和黄绵土表层的膨胀性,其影响的程度和方向取决于土壤类型。同时,BSCs的膨胀性受含水量、温度、干湿以及冻融循环等关键因素影响。     

随机海浪的列率谱分析研究

首次将列率谱分析的方法引入到随机海浪的研究。根据不同环境条件下实验室资料，对列率谱与频率谱进行了比较。结果表明，列率频对频率谱高频部分有明显改善，特别对二倍频含能段的分辨具有明显的优越性。另外，列率谱具有计算速度快、简单易行、分辨率高等优点。     

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.     

A) A. I. H. S. / A) A. I. H. S.

Abstract

A new method is presented to generate stationary multi-site hydrological time series. The proposed method can handle flexible time-step length, and it can be applied to both continuous and intermittent input series. The algorithm is a departure from standard decomposition models and the Box-Jenkins approach. It relies instead on the recent advances in statistical science that deal with generation of correlated random variables with arbitrary statistical distribution functions. The proposed method has been tested on 11 historic weekly input series, of which the first seven contain flow data and the last four have precipitation data. The article contains an extensive review of the results.

Editor D. Koutsoyiannis

Citation Ilich, N., 2014. An effective three-step algorithm for multi-site generation of stochastic weekly hydrological time series. Hydrological Sciences Journal, 59 (1), 85–98.     

基于数值预报和随机森林算法的强对流天气分类预报技术

随机森林算法是当前得到较为广泛应用的机器学习方法之一,有着很高的预测精度,训练结果稳定,泛化能力强,解决多分类问题有明显优势。本文将随机森林算法应用于强对流的潜势预测和分类,分短时强降水、雷暴大风、冰雹和无强对流四种类别,基于2005—2016年NCEP 1°×1°再分析资料计算的对流指数和物理量,开展强对流天气的分类训练、0～12 h预报和检验,经2015—2016年独立测试样本检验表明,针对强对流发生站点的点对点检验,整体误判率为21. 9%,85次强对流过程基本无漏报,模型尤其适用于较大范围强对流天气。随机森林算法筛选的因子物理意义较为明确,和主观预报经验基本相符,模型准确率高,可用于日常业务。