南海热带气旋的气候变化及强度预测方法研究

建立南海海域1949～2007年6～10月份热带气旋（以下简称TC）年、月频数和TC中心强度的历史资料统计文件,分析TC的年月变化。结果表明：近50年,TC具有10a左右的周期变化,1964～1974年和1985～1995年为南海两个强台风以上级别频发期,1997～2006年为TC频数少且强度弱的时期。同时TC强度的空间分布分析结果表明,中沙北部海域和东沙西部海域为强台风多发生区,各月TC强度分布特征明显不同,且其加强通道具有南-北-南阶段性变化。另外,通过分别对1949～2007年北半球500hPa高度场及海温场的格点资料和TC强度历史资料的相关计算,选取高相关格点,根据相关权重组成组合因子,构建二次型预测方程,做年月TC强度预测。预测检验结果显示,冬季的高度场和海温场对次年的TC强度预测效果良好。     

港域波浪数学模型的改进与验证

通过物理模型对改进的港内波浪传播变形数学模型进行验证。该数学模型以推广的时变缓坡方程为控制方程,采用含松弛因子的ADI法求解,并对波浪反射和透射边界模拟方法进行改进。先通过物理模型试验确定斜向浪入射条件下抛石防波堤前的波浪反射系数,作为数学模型中部分反射边界模拟的依据。然后进行了一个典型港口内波浪折射、绕射和反射的模型试验,测量港内波浪分布。对比模型试验和数学模型计算的结果表明,数学模型可较好地模拟港内复杂地形和边界条件下规则波和不规则波的传播变形。     

金沙江下游多种面雨量集成预报方法的对比分析

集成方法有利于提高降水要素预报的准确性和可预报性。本文基于格点实况资料和智能网格预报、西南区域数值预报、ECMWF模式预报、GRAPES模式预报产品,以面雨量为研究对象,采用多元回归法、BP神经网络法、评分权重法、加权集成预报法和算术平均法,得到集成面雨量预报,再运用平均绝对误差、模糊评分、正确率、TS评分、偏差分析等方法,对2020年4—10月金沙江下游面雨量预报效果进行对比分析。结果表明：多元回归集成法和BP神经网络法的预报效果总体上优于其他几种集成方法。在考虑流域面雨量的预报量级时,下游可以采用预报量级较小的模式和集成方法。集成后偏差百分比均有降低,且多元回归法和BP神经网络法对预报量级较小的模式有矫正作用。在面雨量有无、小雨和中雨预报中,多元回归法集成效果较好,在大雨量级预报中,BP神经网络法集成效果较好。这些结论可为流域面雨量预报提供参考借鉴。     

质点-网格海冰模式在渤海的数值预报试验

在海冰动力学和热力学的研究基础上,开发应用于渤海的质点-网格海冰模式.该模式采用质点-网格法,有效地避免了传统海冰模式的数值扩散问题.该模式采用冰厚分布函数取代传统渤海业务海冰预报模式中平整冰、堆积冰和开阔水的三类海冰分型.应用质点-网格海冰模式对2003年～2004年冬季渤海冰情进行了业务化逐日数值预报试验,并对预报结果进行统计检验和分析比较.     

郯庐断裂带(安徽段)及邻区的动力学分析与区域构造演化

依据区域构造层次划分,采用构造筛分法,层层深入,层层筛分,确定发生于各个不同时代地层/岩层内的断裂活动的同期及叠加的应力场特征。综合所有的同期应力场特征及辅以叠加的应力场特征来验证,从而确定了一个连续的、完整的断裂活动的应力场演化序列;结合区域构造变形特征分析,阐明郯庐断裂带(安徽段)的构造演化。应力场分析显示:晚三叠-早侏罗世应力场为北北西—南南东或近南北向挤压,属古特提斯构造域,断裂发生同造山走滑;早白垩世早期,应力场为北西—南东向挤压,断裂发生左行走滑运动,中国东部处于西环太平洋构造域;早白垩世晚期—古新世(始新世),区域发生北西—南东向伸展作用,断裂处于伸展断陷作用阶段;新生代,受区域上近东西向的挤压作用影响,断裂发生挤压逆冲兼右行走滑作用。     

PRELIMINARY STUDY OF BAROTROPIC STOCHASTIC DIFFERENCE MODEL APPLIED TO WEATHER PREDICTION

In order to consider both the deterministic and the stochastic property of atmospheric motion simul-taneously,in this paper,the weather prediction is proposed as the problem of the evolution of meteorologicalfield.The historical viewpoint of atmospheric motion is emphasized here.Based on time series analysis te-chnique,a stochastic-dynamical model with multiple initial fields is derived.Thus,weather forecasting is sum-meal up as a problem of solving a set of stochastic difference equations.For the barotropic atmosphere,thenumerical solutions of the equations are obtained by using the method of empirical orthogonal functions(EOF),and examples of medium-range weather prediction are given here.Meanwhile,selecting the order oftime series,i.e.,determining the number of initial fields properly,is also discussed.     

有限区域短期数值预报业务系统简介

简要介绍了空军气象中心投入业务使用的《空军有限区域短期数值预报业务系统》的概况、采用的主要技术，检验评分结果，最后给出了天气学个例检验结果。     

DIAGNOSTIC ANALYSIS AND VERIFICATION OF PREDICTION OF THE TROPICAL PACIFIC SEA SURFACE TEMPERATURE ANOMALIES DURING 1997 ~ 1998

By comparing with ENSO events that ever happened in the history, the basic features and probable causes of the anomalous sea surface temperature of the tropical Pacific Ocean during 1997 and 1998 have been analyzed diagnostically. It is found that the 1997/1998 El Nino had significant abnormalities and peculiarities. It differs from the previous El Ni?o events falling into the simple “eastern pattern” or “western pattern”. The predictions of 1997/1998 El Ni?o event have also been tested with an intermediate ocean-atmosphere coupled dynamic model. The results show that the skills of the 0～24 lead month forecasts for the warm event are all above 0.5. The predictions of the mature phase and the later stages of the warm event are better than those of the beginning phase.     

Assessing spatial and attribute errors in large national datasets for population distribution models: a case study of Philadelphia county schools

Geospatial technologies and digital data have developed and disseminated rapidly in conjunction with increasing computing efficiency and Internet availability. The ability to store and transmit large datasets has encouraged the development of national infrastructure datasets in geospatial formats. National datasets are used by numerous agencies for analysis and modeling purposes because these datasets are standardized and considered to be of acceptable accuracy for national scale applications. At Oak Ridge National Laboratory a population model has been developed that incorporates national schools data as one of the model inputs. This paper evaluates spatial and attribute inaccuracies present within two national school datasets, Tele Atlas North America and National Center of Education Statistics (NCES). Schools are an important component of the population model, because they are spatially dense clusters of vulnerable populations. It is therefore essential to validate the quality of school input data. Schools were also chosen since a validated schools dataset was produced in geospatial format for Philadelphia County; thereby enabling a comparison between a local dataset and the national datasets. Analyses found the national datasets are not standardized and incomplete, containing 76 to 90 percent of existing schools. The temporal accuracy of updating annual enrollment values resulted in 89 percent inaccuracy for 2003. Spatial rectification was required for 87 percent of NCES points, of which 58 percent of the errors were attributed to the geocoding process. Lastly, it was found that by combining the two national datasets, the resultant dataset provided a more useful and accurate solution.