包含冻结过程的广义位温及位涡特征分析

为了对比分析降水过程中不同表达形式热力学变量和位涡时空分布特点,本文针对2017年7月13～14日吉林省强降水过程,利用模式输出资料对常规位温（θ）、相当位温（θ_e）、包含凝结概率函数的广义位温（θ_Gao）、包含冻结概率函数的广义位温（θ_Wang）和同时涵盖凝结过程与冻结过程（θ_Gu）五种不同形式位温进行计算,并分析五种对应位涡[PV(θ)、PV(θ_e)、PV(θ_Gao)、PV(θ_Wang)、PV(θ_Gu)]与降水的关系。结果表明,引入冻结概率函数的广义位温（θ_Wang）和对应的广义湿位涡PV(θ_Wang)与强降水的对应性更好。θ_Wang与θ_Gao差异集中在降水区对流层中高层5～11 km,前者始终高于后者,最大差异达2.5 K,说明冻结概率函数的引入扩大了广义位温的适用范围,更适合描述降水区湿大气非均匀饱和热力状态。五种位涡的差异主要在降水区上空12 km以下,由θ_Gao和θ_Wang定义的位涡PV(θ_Gao)和PV(θ_Wang)的正负异常中心更为明显。相比于PV(θ_Gao)和PV(θ_Wang)异常值更大,差异可达±0.2 PVU,这主要是由于冻结概率函数的引入增大降水区上空广义位温,促使冻结区的湿位涡异常增强。     

Shared near neighbours neural network model: a debris flow warning system

The main purpose of this study is to develop a new type of artificial neural network based model for constructing a debris flow warning system. The Chen‐Eu‐Lan river basin, which is located in Central Taiwan, is assigned as the study area. The creek is one of the most well‐known debris flow areas where several damaging debris flows have been reported in the last two decades. The hydrological and geological data, which might have great influence on the occurrence of debris flows, are first collected and analysed, then, the shared near neighbours neural network (SNN + NN) is presented to construct the debris flow warning system for the watershed. SNN is an unsupervised learning method that has the advantage of dealing with non‐globular clusters, besides presenting computational efficiency. By using SNN, the compiled hydro‐geological data set can easily and meaningfully be clustered into several categories. These categories can then be identified as ‘occurrence’ or ‘no‐occurrence’ of debris flows. To improve the effectiveness of the debris flow warning system, a neural network framework is designed to connect all the clusters produced by the SNN method, whereas the connected weights of the network are adjusted through a supervised learning method. This framework is used and its applicability and practicability for debris flow warning are investigated. The results demonstrate that the proposed SNN + NN model is an efficient and accurate tool for the development of a debris flow warning system. Copyright © 2007 John Wiley & Sons, Ltd.     

Development of a new photon diffraction imaging system for diagnostic nuclear medicine

The objective of this project is to develop and construct an innovative imaging system for nuclear medicine and molecular imaging that uses photon diffraction and is capable of generating 1–2 mm spatial resolution images in two or three dimensions. The proposed imaging system would be capable of detecting radiopharmaceuticals that emit 100–200 keV gamma rays which are typically used in diagnostic nuclear medicine and in molecular imaging. The system is expected to be optimized for the 140.6 keV gamma ray from a Tc-99m source, which is frequently used in nuclear medicine. This new system will focus the incoming gamma rays in a manner analogous to a magnifying glass focusing sunlight into a small focal point on a detector's sensitive area. Focusing gamma rays through photon diffraction has already been demonstrated with the construction of a diffraction lens telescope for astrophysics and a scaled-down lens for medical imaging, both developed at Argonne National Laboratory (ANL). In addition, spatial resolutions of 3 mm have been achieved with a prototype medical lens. The proposed imaging system would be comprised of an array of photon diffraction lenses tuned to diffract a specific gamma ray energy (within 100–200 keV) emitted by a common source. The properties of photon diffraction make it possible to diffract only one specific gamma ray energy at a time, which significantly reduces scattering background. The system should be sufficiently sensitive to the detection of small concentrations of radioactivity that can reveal potential tumor sites at their initial stages of development. Moreover, the system's sensitivity would eliminate the need for re-injecting a patient with more radiopharmaceutical if this patient underwent a prior nuclear imaging scan. Detection of a tumor site at its inception could allow for an earlier initiation of treatment and wider treatment options, which can potentially improve the chances for cure.     

Regional economic impact analysis of earthquakes in northern Taiwan and its implications for disaster reduction policies

The direct damage caused by earthquakes, such as impaired buildings, may interfere with normal business operations and disrupt the function of the industrial chain. Such economic impacts can be evaluated using the input–output analysis developed by Leontief. In this paper, two scenario earthquakes in northern Taiwan both with a return period of 475 years—the Hsinchu Hsincheng and the Yilan Nan-ao earthquakes—are simulated. The results show that the economic impact caused by the Hsincheng earthquake is greater than that resulting from the Nan-ao earthquake, which should be the major scenario considered for the disaster reduction plan. The industries affected the most are the manufacturing, food services and entertainment, storage and retail trade, and public and construction industries. The Nan-ao earthquake causes relatively more losses in the food services and entertainment industries. Most of the repercussion effects of these industries are in the central and southern parts of Taiwan. The loss to the manufacturing sector and its repercussion effects are enormous. Therefore, the government should make it a first priority to encourage the manufacturing sector to implement earthquake mitigations, such as a seismic retrofit, or to provide a seismic evaluation, which can enable firms to engage in mitigation voluntarily. The measure needed to reduce the loss in agriculture is that the government can purchase agricultural products in central and southern Taiwan following the disaster and offer them to survivors in northern Taiwan.     

高阶统计量在探地雷达数据处理中的应用

用高阶统计量方法对探地雷达数据进行处理,基本思路是采用信号识别与检测中的高阶统计量参数,对不同模型的探地雷达数据记录进行处理与重构,结果表明不同模型具有不同阶数的高阶统计量特征。模型实验与实际应用都表明,高阶统计量对压制背景噪声和多次反射波有较好的效果。     

ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil

In reality, footings are most likely to be founded on multi-layered soils. The existing methods for predicting the bearing capacity of 4-layer up to 10-layer cohesive soil are inaccurate. This paper aims to develop a more accurate bearing capacity prediction method based on multiple regression methods and multi-layer perceptrons (MLPs), one type of artificial neural networks (ANNs). Predictions of bearing capacity from the developed multiple regression models and MLP in tractable equations form are obtained and compared with the value predicted using traditional methods. The results indicate ANNs are able to predict accurately the bearing capacity of strip footing and outperform the existing methods.     

CLIMATOLOGICAL VARIATION FEATURES OF TYPHOON PRECIPITATION INFLUENCING FUJIAN FOR THE PAST 46 YEARS

The results of an analysis of the temporal and spatial distribution of typhoon precipitation influencing Fujian from 1960 to 2005 show that typhoon precipitation in Fujian province occurs from May to November, with the most in August. There has been a decreasing trend since 1960. Typhoon precipitation gradually decreases from the coastal region to the northwestern mainland of Fujian and the maximum typhoon precipitation occurs in the northeast and the south of Fujian. Typhoon torrential rain is one of the extreme rainfall events in Fujian. High frequencies of typhoon torrential rain occur in the coastal and southwest regions of the province. With the impact of Fujian’s terrain, typhoon precipitation occurs more easily to the east of the mountains than to the west. Atmospheric circulation at 500 hPa over Asia and sea surface temperature anomalies of the equatorial eastern Pacific are analyzed, with the finding that they are closely connected with the anomaly of typhoon precipitation influencing Fujian, possibly mainly by modulating the northbound track of typhoons via changing the atmosphere circulation to lead to the anomaly of typhoon precipitation over the province.     

Assimilation and Simulation of Typhoon Rusa （2002） Using the WRF System

Using the recently developed Weather Research and Forecasting (WRF) 3DVAR and the WRF model, numerical experiments are conducted for the initialization and simulation of typhoon Rusa (2002).The observational data used in the WRF 3DVAR are conventional Global Telecommunications System (GTS) data and Korean Automatic Weather Station (AWS) surface observations. The Background Error Statistics (BES) via the National Meteorological Center (NMC) method has two different resolutions, that is, a 210-km horizontal grid space from the NCEP global model and a 10-km horizontal resolution from Korean operational forecasts. To improve the performance of the WRF simulation initialized from the WRF 3DVAR analyses, the scale-lengths used in the horizontal background error covariances via recursive filter are tuned in terms of the WRF 3DVAR control variables, streamfunction, velocity potential, unbalanced pressure and specific humidity. The experiments with respect to different background error statistics and different observational data indicate that the subsequent 24-h the WRF model forecasts of typhoon Rusa‘s track and precipitation are significantly impacted upon the initial fields. Assimilation of the AWS data with the tuned background error statistics obtains improved predictions of the typhoon track and its precipitation.     

黑潮营养盐输入对东海陆架浮游生态系统影响的模型研究

黑潮是东海陆架主要的外源营养盐来源之一。本文建立了一个物理-生化耦合的三维生态动力学模型,通过敏感性实验分析了黑潮营养盐输入对东海浮游生态系统的影响,分区域、水层和季节对黑潮输入营养盐的贡献进行了统计分析。主要得到如下结论:(1)黑潮对东海陆架营养盐的影响主要集中在整个外陆架和中陆架的中北部,从垂向分布来看,对外陆架影响主要在50m以深的中下层,而对中陆架的影响在次表层(20—50m)和中下层都较为显著,黑潮对东海陆架营养盐输入的总量,冬季略高于夏季。(2)黑潮对东海浮游植物的贡献冬夏两季存在较大差异,冬季叶绿素的增量主要集中在外陆架的中北部和中陆架的中部,分布在表层(0—20m)和次表层;而夏季则主要在外陆架的中部、中陆架的中北部和内陆架的北部,除了内陆架北部以外都以次表层为主。