大气环境质量预测与计算机技术的结合--"大气污染扩散空间信息系统研究"小论

简述了在“大气污染扩散空间信息系统研究”项目中 ,大气环境质量预测的理论和计算机技术有机结合起来 ,达到了取长补短的目的     

两种定性天气预报模型的对比分析

以南京１９６５～１９９４ 年４ 月平均气温作为预报量, 选取前期５００ ｈ Ｐａ 月平均高度场相关因子, 分别建立了事件概率回归预报模型和神经网络预报模型。通过对比分析发现, 在同等条件下,由于神经网络方法能更好地反映预报量与预报因子间的非线性关系, 并能有效避免采用事件概率回归方法预报建模时, 对预报因子分级造成信息损失的缺点。因此, 其拟合和预报效果明显优于传统的概率回归预报方法     

乌鲁木齐市大气中SO2的灰色预测

根据灰色系统理论,联系乌鲁木齐市大气环境的实际状况,建立了精度检验为一级的灰色预测模型,并对大气中的SO2值进行了预测分析,结果表明,该预测模型具有较高的准确性、合理性和可信度.     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

影响上海地区热带气旋频数的预测水平评估

在文献「１」研究的基础上,探讨了热带气旋频数预测水平的评估方法,定义了三个更适合于评估每年热带气旋预测性能的参数,对影响上海地区热带气旋频数的预测水平进行了评估,并与“九五”攻关前的业务预测性进行了比较。     

洪水前兆的初步探讨

根据历史洪水和现有研究 ,比较系统地分析了各种洪水前兆 ,可以为洪水预报提供一定的理论依据 ,同时指出 ,为了提高预报精度 ,必须对洪水前兆进行综合分析 ,去伪存真 ,最终达到防洪减灾的目的     

系统仿真方法在环境规划预测中的应用

讨论了环境规划中区域社会-经济-环境模型建立的原则，提出了适合非定态城市发展过程中规划因子的预测方法，并给出了江苏靖江“十一五”期间环境规划中的具体应用。     

基于SARIMA模型和普通Kriging法对杭州市主城区PM2.5短期预测和制图

基于SARIMA模型对杭州市主城区内的7个站点和周边3个站点的PM_(2.5)浓度进行4 h平均的短期预报,并利用普通Kriging法对杭州市主城区PM_(2.5)进行空间插值和制图.在建立SARIMA模型时,用批量自动化的方法,使用R语言编程对夏季和冬季各360期的数据进行SARIMA(p,d,q)×(P,D,Q)6模型的参数的确定和拟合,来预测下一期的值.在10个站点分别进行120期的预测,做出真实与预测的时间序列图,在精度分析中,夏季和冬季PM_(2.5)浓度总体的平均绝对误差(MAE)分别为8.4μg·m~(-3)和14.8μg·m~(-3).在Arc GIS中,对每期的数据使用普通Kriging法,用球面模型拟合半变异函数,决定各个站点的权重,来对杭州市区内未知空间样点进行插值,最后生成完整的杭州市主城区PM_(2.5)短期预测图.本研究创新性地将SARIMA模型广义化运用到小尺度时间序列中,预报效果较好,并且批量自动化预测和制图的方法,可为今后的预测制图产品化提供技术支持.     

USING CLIMATE FORECASTS FOR WATER MANAGEMENT: ARIZONA AND THE 1997–1998 EL NIÑO1

ABSTRACT: Unrelenting pressure on limited surface water supplies requires increasingly sophisticated water management approaches. Climate forecasts of seasonal precipitation and temperature are potentially useful, but the operational water management community currently underutilizes them. However, some agencies in Arizona took unprecedented advantage of forecasts for a potentially wet winter during the 1997–1998 El Niño event. This study investigates use of this information through a series of semi‐structured in‐depth interviews with key personnel from agencies responsible for emergency management and water supply; their jurisdictions ranged from urban to rural and local to regional. Interviews investigated information acquisition, interpretation, and incorporation into specific decisions and actions. While unprecedented actions were taken by some water management agencies and no agencies implemented inappropriate measures, some missed opportunities for more effective response, primarily through inaction. This study reveals a variety of technical factors and institutional characteristics affecting forecast use. Study findings emphasize the need for: (a) closer ongoing relationships between forecast producers and users, (b) increased institutional flexibility to exploit the increasing skill of seasonal climate forecasts, (c) demonstration projects of effective forecast use, and (d) a regional forum to facilitate information transfer between the hydro‐climatic research community and operational water managers.     

Using Paleo Reconstructions to Improve Streamflow Forecast Lead Time in the Western United States

In water stressed regions, water managers are exploring new horizons that would help in long‐range streamflow forecasts. Oceanic‐atmospheric oscillations have been shown to influence streamflow variability. In this study, long‐lead time streamflow forecasts are made using a multiclass kernel‐based data‐driven support vector machine (SVM) model. The extended streamflow records based on tree ring reconstructions were used to provide a longer time series data. Reconstructed data were used from 1658 to 1952 and the instrumental record was used from 1953 to 2007. Reconstructions for oceanic‐atmospheric oscillations included the El Niño‐Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, and North Atlantic Oscillation. Streamflow forecasts using all four oscillations were made with one‐year to five‐year lead times for 21 gages in the western United States. This is the first study that uses both instrumental and reconstructed data of oscillations in SVM model to improve streamflow forecast lead time. SVM model was able to provide “satisfactory” to “very good” forecasts with one‐ to five‐year lead time for the selected gages. The use of all the oscillation indices helped in achieving better predictability compared to using individual oscillations. The SVM modeling results are better when compared with multiple linear regression model forecasts. The findings are statistical in nature and are expected to be useful for long‐term water resources planning and management.