四川凉山平川镇“7.14”泥石流灾害的气象成因

以盐源县的泥石流发生为例,研究了凉山地区出现局地强降雨引发的中型泥石流灾害的气象成因。运用天气学方法,使用NCEP再分析资料以及卫星云图和雷达回波等遥感资料,从环流形势、强对流不稳定、卫星云图和雷达回波图像演变特征,研究了台风登陆时凉山地区强降雨引起泥石流灾害形成的气象成因,并讨论了应用多普勒雷达回波对局地强降水及泥石流灾害短时临近监测和预报预警的思路。     

西藏高原短临天气预报预警系统构建研究

参考气象信息综合分析处理系统和卫星广域网灾害天气短时临近预报预警系统的界面操作特征,利用现有卫星遥感资料、自动站资料、闪电定位资料、探空资料和常规气象要素资料等观测资料,以及已研制的有关西藏高原的降水预报预警模型、物理统计诊断模型和三维云模式预报模型等,同时考虑西藏高原地区雷达资料应用的地理障碍,设计构建了西藏高原短临天气预报预警系统。通过可视化简单编程指令代码语言环境实现了能集成气象信息综合分析处理系统的信息分析功能、适合西藏高原的短临天气预报预警的实用系统。     

Nash model parameter uncertainty analysis by AM-MCMC based on BFS and probabilistic flood forecasting

A hydrologic model consists of several parameters which are usually calibrated based on observed hydrologic processes. Due to the uncertainty of the hydrologic processes, model parameters are also uncertain, which further leads to the uncertainty of forecast results of a hydrologic model. Working with the Bayesian Forecasting System (BFS), Markov Chain Monte Carlo simulation based Adaptive Metropolis method (AM-MCMC) was used to study parameter uncertainty of Nash model, while the probabilistic flood forecasting was made with the simulated samples of parameters of Nash model. The results of a case study shows that the AM-MCMC based on BFS proposed in this paper is suitable to obtain the posterior distribution of the parameters of Nash model according to the known information of the parameters. The use of Nash model and AM-MCMC based on BFS was able to make the probabilistic flood forecast as well as to find the mean and variance of flood discharge, which may be useful to estimate the risk of flood control decision.     

渤海海湾纳潮量现状卫星遥感调查与分析

利用14景环境与灾害监测预报星(HJ-1A/1B)CCD相机卫星数据,提取不同潮位下渤海海湾的水域面积.结合潮汐订正的方法,计算获得了2010年辽东湾、渤海湾和莱州湾的纳潮量分别为9.32x1010m3、3.03x1010m3和0.54×1010m3.计算结果将为渤海动力学研究提供参考.     

基于主成分回归分析法预测中国铁矿石需求

在论述铁矿石需求预测途径的基础上,选取影响我国铁矿石需求的8个基本因素,采用回归分析方法进行了铁矿石需求的单因素分析.单因素分析结果表明,选取的8个基本因素与铁矿石需求的相关度基本都大于0.9.对8个基本影响因素进行了主成分分析,最终降维为4个主成分.将主成分分析方法与回归分析方法相结合,建立了铁矿石的需求预测模型,并对我国2015年和2020年铁矿石的需求量进行了预测,分别为29.76亿t和26.68亿t.     

1960-2009年新疆渭干河流域蒸发皿蒸发量变化特征

根据新疆气象局地面观测资料,整理出渭干河流域1960-2009年蒸发皿蒸发量资料,运用线性趋势估计、距平与累积距平、滑动t检验、小波分析、灰色预测等方法,对蒸发皿蒸发量的变化趋势、周期特征、突变特征及未来趋势进行了分析。结果表明:①50年来,渭干河流域蒸发皿蒸发量有升有降,总体上呈波动式减少的趋势,其变化倾向率为-19.72 mm/10a,通过了0.05的显著性检验。累积距平曲线的变化趋势显示,年蒸发皿蒸发量以1987年和2004年为转折点,前期偏多,中期偏少,后期偏多。②年蒸发皿蒸发量在1987年发生了由多到少的突变,在2004年发生了由少到多的突变,突变类型比较复杂,是均值突变和转折突变两种突变类型的组合。③蒸发皿蒸发量在50a的时间序列中存在8a、17a和20a的周期。在8a的时间尺度上,年蒸发皿蒸发量序列经历了7个偏多期和6个偏少期;在17a的时间尺度上,经历了2个偏多期和2个偏少期;在20a的时间尺度上,经历了1个偏少期和2个偏多期。④利用建立的灰色预测GM(1,1)模型对渭干河流域蒸发皿蒸发量进行了预测,发现模型的可靠性高,预测结果准确,年蒸发皿蒸发量在未来3年将出现增加的趋势。     

基于支持向量回归机的耕地保有量组合预测

为提高耕地保有量预测精度,将灰色预测GM(1,1)模型、动力预测模型、BP网络预测模型和加权支持向量回归机预测模型相结合,建立了基于支持向量回归机的耕地保有量组合预测模型,并将其用于温州市耕地保有量预测。结果表明,该模型比任一单一预测模型精度更高,可用于耕地保有量预测。     

Performance of an artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a tropical site

Performance of a feed‐forward back‐propagation artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a single meteorological station is presented. Both short‐term and long‐term forecasting was attempted, with ground level data collected by the meteorological station in Colombo, Sri Lanka (79° 52′E, 6° 54′N) during two time periods, 1994–2003 and 1869–2003. Two neural network models were developed; a one‐day‐ahead model for predicting the rainfall occurrence of the next day, which was able to make predictions with a 74·3% accuracy, and one‐year‐ahead model for yearly rainfall depth predictions with an 80·0% accuracy within a ± 5% error bound. Each of these models was extended to make predictions several time steps into the future, where accuracies were found to decrease rapidly with the number of time steps. The success rates and rainfall variability within the north‐east and south‐west monsoon seasons are also discussed. Copyright © 2007 John Wiley & Sons, Ltd.     

An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons

Inflow forecasting is essential for decision making on reservoir operation during typhoons. In this paper, a radial basis function (RBF)‐based model with an information processor is proposed for more accurate forecasts of hourly reservoir inflow. Firstly, based on the multilayer perceptron neural (MLP) network, an information processor is developed to pre‐process the typhoon information (namely, typhoon characteristics and rainfall) and to produce forecasts of rainfall. The forecasted rainfall and the observed inflow are then used as input to the RBF‐based model, which is a nonlinear function approximator, to produce forecasts of hourly inflow. For parameter estimation of the RBF‐based model, the fully‐supervised learning algorithm is used. Actual applications of the proposed model are performed to yield 1‐ to 6‐h ahead forecasts of inflow. To assess the improvement due to the use of the typhoon information processor, models without the typhoon information processor are constructed and compared with the proposed model. The results show that the proposed model performs the best and is capable of providing improved forecasts of hourly inflow, especially for long lead‐time. In conclusion, the proposed model with a typhoon information processor can extract useful information from typhoon characteristics and rainfall, and consequently improve the forecasting performance. Copyright © 2009 John Wiley & Sons, Ltd.     

River suspended sediment modelling using a fuzzy logic approach

Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating‐curve models. Benchmarking was based on a 5‐year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating‐curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright © 2006 John Wiley & Sons, Ltd.