数值预报产品动力-统计释用方法与寒潮预报

从气温变化的物理过程出发,设计了一种数值预报产品的动力－统计释用方法,开发了一套四川盆地寒潮入侵时间和降温幅度的释用方案,并利用ＥＣＭＷＦ产品建立四川盆地寒潮自动预报系统,滚动预报未来１～６日内的寒潮过程。１９９５～１９９７年投入中短期业务运行,取得了显著效果。     

河西内陆河春季流量变化特征的分析和预测

利用昌马、莺落峡、九条岭３个水文站四十余年的月平均流量资料,分析河西地区３条主要内陆河春季流量的长期变化特征,并用均生函数模型对流量进行了预报,为河西地区水资源的研究和利用提供参考依据     

基于对流可分辨集合预报的阜宁龙卷概率预报研究

本文针对2016年6月23日江苏阜宁龙卷,设计了两组对流可分辨尺度集合预报:一组以ERA5再分析资料为初始和侧边界(CEFS_ERA5);另一组以NCEP GEFS为初始和侧边界(CEFS_GEFS),评估了两组试验对此次龙卷的预报能力。结果显示:两组对流尺度集合预报均有约半数以上成员能够再现龙卷超级单体的特征;2~5 km上升螺旋度(UH25)对本次龙卷超级单体有较好的预报指示意义。在上述分析的基础上,考虑位置预报偏差,提出了一种基于UH25的邻域龙卷概率预报产品,分析了龙卷概率预报技巧对关键参数邻域半径和UH25阈值的敏感性,CEFS_ERA5邻域半径取15个格点,UH25阈值取250 m²·s^-2最优;而CEFS_GEFS邻域半径取15个格点,UH25阈值取100 m²·s^-2最优。总的来说,邻域概率预报产品显著提升了对此次龙卷概率预报水平。     

Application of linear and nonlinear techniques in river flow forecasting in the Kilombero River basin,Tanzania / Application de techniques linéaires et non-linéaires à la prévision des débits dans le bassin de la Rivière Kilombero,en Tanzanie

Abstract

Modelling of the rainfall–runoff transformation process and routing of river flows in the Kilombero River basin and its five sub-catchments within the Rufiji River basin in Tanzania was undertaken using three system (black-box) models—a simple linear model, a linear perturbation model and a linear varying gain factor model—in their linear transfer function forms. A lumped conceptual model—the soil moisture accounting and routing model—was also applied to the sub-catchments and the basin. The HEC-HMS model, which is a distributed model, was applied only to the entire Kilombero River basin. River discharge, rainfall and potential evaporation data were used as inputs to the appropriate models and it was observed that sometimes the system models performed better than complex hydrological models, especially in large catchments, illustrating the usefulness of using simple black-box models in datascarce situations.     

Application of integrated back‐propagation network and self organizing map for flood forecasting

Much of the nonlinearity and uncertainty regarding the flood process is because hydrologic data required for estimation are often tremendously difficult to obtain. This study employed a back‐propagation network (BPN) as the main structure in flood forecasting to learn and to demonstrate the sophisticated nonlinear mapping relationship. However, a deterministic BPN model implies high uncertainty and poor consistency for verification work even when the learning performance is satisfactory for flood forecasting. Therefore, a novel procedure was proposed in this investigation which integrates linear transfer function (LTF) and self‐organizing map (SOM) to efficiently determine the intervals of weights and biases of a flood forecasting neural network to avoid the above problems. A SOM network with classification ability was applied to the solutions and parameters of the BPN model in the learning stage, to classify the network parameter rules and to obtain the winning parameters. The outcomes from the previous stage were then used as the ranges of the parameters in the recall stage. Finally, a case study was carried out in Wu‐Shi basin to demonstrate the effectiveness of the proposal. Copyright © 2009 John Wiley & Sons, Ltd.     

Experimental research on mechanical characteristics and strength criterion of rock of triaxial unloading tests under different stress pathsEI北大核心CSCD

By carrying out triaxial unloading test under different stress paths on rock, the complete stress-strain curves, deformation characteristics and its strength criterion are studied. The test results show that the relation between lateral strain and confining pressure firstly is linear, and then is nonlinear in the total stage of unloading confining pressure. And its growth rate is about 3 to 5 times the growth rate of the axial strain. It shows obvious lateral dilatancy, and the degree of dilatancy is related to the unloading path. According to the area that is enclosed by confining pressure and volumetric strain curve, it can be seen that the greater the confining pressure is before unloading, the more the energy releases. Deformation modulus decreases gradually with the unloading confining pressure, and increases with the initial confining pressure; the relation between deformation modulus and confining pressure shows negative exponential distribution overall. At the same unloading paths, the reduction of the deformation modulus increases with the increase of initial confining pressure. The Poisson's ratio rises up continually, and the variation law with the confining pressure can be well described by nonlinear. Failure characteristics of rock are mainly shear failure. The strength characteristics of rock can be well characterized by using the Mohr strength criterion of power function. These conclusions provide reliable theoretical reference and guiding significance for the deep mining of the underground metal mine.     

目标样本粒度判据在海湾环境演变研究中的运用——以福建前湖湾为例

高密度采集福建前湖湾海岸剖面133个泥沙样品做粒度分析,其频率曲线显示多种粒度分布特征,预示多种沉积环境的变化。采集研究剖面周边海滩、河口浅滩、河口、滨海沼泽和海岸沙丘等已知环境的样品作为目标样本。将目标样本沉积参数平均粒径(Mz)、标准偏差(σ)、峰态(Kg)设为判别参数,利用Excel计算海岸剖面样品与目标样本参数的距离,筛选最小距离样品,归为已知目标样本的同类。据此,分析了海岸剖面蕴含的6个沉积环境变化阶段。利用剖面底部淤泥测年14C=(328 15±170)a BP,和剖面上部泥炭测年14C=(24 130±100)a BP,了解这一变化发生的年代。自(32 815±170)a BP以来,前湖湾经历了河口浅滩-河口、沙丘-河口-滨海沼泽-海滩-沙丘等海岸环境演变过程。     

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???￥??GNSS??????????????????????????????μ???????????λ??仯??????????ɡ??????????????λ??仯????????????г??????????￥?????λ????????????????ü????????????????????????????????λ???????????????RMS????3 mm?????????RMS?????7 mm??     

基于高速公路气象监测数据分析的浓雾预报指标

利用京秦高速公路沿线交通气象监测站实况资料,通过对84个站次的浓雾雾生和雾消各气象要素变化特征进行分析,归纳出高速公路沿线浓雾和强浓雾天气雾生雾消的预报指标。爆发性强浓雾期间能见度少波动,在能见度爆发下降前,温度下降过程中的小幅上升对能见度突然下降有很好的指示作用;相对湿度在能见度爆发下降前1 h内达到80%以上。一般性强浓雾大多数出现在温度波动之后继续直线下降期间;在500 m浓雾出现15 h之前空气相对湿度达90%以上,能见度达50 m之前相对湿度基本达饱和状态。浓雾消散主要有两个方面,因冷空气造成的雾消,预报应着眼于冷空气前锋影响高速公路所在区域的时间;而由辐射升温造成的雾消,预报应着眼于对天空状况和升温速度的判断。     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.