副高后部暴雨天气概念模型

1 引言 由副热带高压后部造成的暴雨,我们简称为副高后部暴雨,它是造成我省夏季暴雨、尤其是大范围区域暴雨和大暴雨的主要灾害性天气之一。在以往的分析中,对个例讨论的比较多,本文使用近40年资料选取出的副高后部暴雨样本,通过分型建立其天气概念模型,并给出预报提示。

副高活动与四川暴雨

通过对 1980年～ 1998年副高特征及四川暴雨的关系分析 ,得结论可为四川暴雨落区、强度预报提供依据。 1 影响四川暴雨的副高有不连续西伸北抬和突然加强西伸或北抬与突然东撤的现象 ;2 影响四川暴雨的副高型式有 4种 :北部阻塞型 ,中部阻塞型、纬向阻塞型、纬向东移型 ,它们对暴雨落区、强度有预示意义 ;3 1998年长江二度出梅之前、后 ,副高影响四川暴雨的型式是明显不同的 ,出梅之前经纬向东移型为主 ,出梅之后以经向型为主。

吉林永吉2017年三场极端暴雨天气成因及重现期分析

对吉林地区2017年“7.13”、“7.19”和“8.03”三次暴雨洪涝过程的降水特点、环流背景、水汽来源及输送、中尺度系统发展条件及过程、云图及雷达特征等方面进行了全面分析。研究发现:前两次暴雨天气成因极为相似,符合副高纬向型切变暴雨概念模型,低空西南急流和低空切变线的长时间维持非常有利于短时强降水的发生发展;云图显示导致短时强降水的MCS云团产生了“列车效应”,雷达回波显示造成强降水的多单体雷暴为降水效率高的热带降水型。“8.03”过程符合副高经向型切变暴雨概念模型,水汽来源与前两次过程不同,北上台风“海棠”带来的外围水汽沿副高后部向吉林地区输送,为降水提供了充沛水汽。对暴雨重现期的研究显示永吉县暴雨的重现期逐渐缩短,极端降水逐渐增多。

Assimilation of ASAR Data with a Hydrologic and Semi-empirical Backscattering Coupled Model to Estimate Soil Moisture

The most promising approach for studying soil moisture is the assimilation of observation data and computational modeling. However, there is much uncertainty in the assimilation process, which affects the assimilation results. This research developed a one-dimensional soil moisture assimilation scheme based on the Ensemble Kalman Filter (EnKF) and Genetic Algorithm (GA). A two-dimensional hydrologic model-Distributed Hydrology-Soil-Vegetation Model (DHSVM) was coupled with a semi-empirical backscattering model (Oh). The Advanced Synthetic Aperture Radar (ASAR) data were assimilated with this coupled model and the field observation data were used to validate this scheme in the soil moisture assimilation experiment. In order to improve the assimilation results, a cost function was set up based on the distance between the simulated backscattering coefficient from the coupled model and the observed backscattering coefficient from ASAR. The EnKF and GA were used to re-initialize and re-parameterize the simulation process, respectively. The assimilation results were compared with the free-run simulations from hydrologic model and the field observation data. The results obtained indicate that this assimilation scheme is practical and it can improve the accuracy of soil moisture estimation significantly.

Application of neural network to prediction of plate finish cooling temperature

To improve the deficiency of the control system of finish cooling temperature (FCT), a new model developed from a combination of a multilayer perception neural network as the self-learning system and traditional mathematical model were brought forward to predict the plate FCT. The relationship between the self-learning factor of heat transfer coefficient and its influencing parameters such as plate thickness, start cooling temperature, was investigated. Simulative calculation indicates that the deficiency of FCT control system is overcome completely, the accuracy of FCT is obviously improved and the difference between the calculated and target FCT is controlled between -15 ℃ and 15 ℃.

SVM model for estimating the parameters of the probability-integral method of predicting mining subsidence

A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.

Simulation Analysis of Macroeconomic Policy Orientations' Effects on the Industrial Economy——Based on Dynamic CGE Model

There are three different orientations of the macroeconomic policies,namely,factor-expansion,prudent-continuation and structural-adjustment.The paper uses the dynamic CGE model to simulate the effects of the three different orientations on the economy,and arrives at the following conclusions:while the factor-expansion policies can lead to growth of economic output,they also result in the further imbalance in the industrial structure and the "crowding-out effect" on the private investment.In addition,such growth is unsustainable in the long run;the growth of the industrial economy will decline rapidly to a lower level after reaching a certain high level.The prudent-continuation policies refer to the continuation of the current policies that sustain the current trends of the industrial economy,and such policies reduce the growth of the industrial economy to a lower level in the long run.The structural-adjustment policies reduce the economic output at the current stage;however,it can bring a gradual improvement of industrial structure and a balance of the supply and demand.In the long run,the optimized structure will improve the total factor productivity(TFP) and the production-possibility frontier,and thereby realize a longterm steady and rapid growth of the industrial economy.Four policy recommendations are proposed based on the simulation of the models,including taking the structural adjustment as the fundamental orientation of macroeconomic policies,lowering the "floor level" of economic growth with macro regulation,giving play to the fundamental role of market mechanism in structural adjustment and increasing the role of TFP in the growth of the industrial economy.

SOLUTION TO THE STRESS DISTRIBUTION ON THE SURFACE LAYER OF BUSHING ALLOY BY COUPLING BEM WITH ELASTICITY METHOD

In the design of the fatigue strength of dynamically loaded bearing in the equipmentssuch as internal combustion engines and roimg mun, the solution to the stress distribution on thebushing alloy layer is an important and difficult problem. In this paper, a new method has beenproposed by coupling BEM with etheticity method, The algorithm and its implementation were deseribed in details The calculation results verify that this up-dated method can provide us a moresimple and effective tool for solvingthe fatigue stress of the bushing alloy with tangible benefit oftime-saving and high computation accuraey. It may open a new vista in bearing fatigue strength design.

Impact of Rising Oil Prices on the Living Cost in Burkina Faso

Fluctuations of the world oil prices affect economic performance. Outside the impact on the sector of energy production, the rising oil price has consequences on inflationary pressures and a deteriorating fiscal position of Burkina Faso. In this context, studying the impact of rising oil prices on the economy, especially the cost of living of its population has a great interest because although many studies have attempted to link 〈〈oil prices〉~ and 〈〈cost of living~, very few have focused on the specific case of Burkina Faso. This allows us to make our contribution to this construction literature. This contribution will consist to highlight the relation between changes in oil prices and the cost of living in Burkina Faso. Also to be reached, we will find the best indicator to reflect the cost of living in Burkina Faso, identify the suitable econometric model for estimating the correlation and verify the existence of the relation between oil prices and the cost of living. For a better approach to this study, we used a VAR （Vector Auto-Regressive） model. Also, we will use documentary research that will make an assessment on the existing in terms of theoretical debates around the theme descriptive statistics that will help to introduce and describe the variables used in the study, and econometric analysis will analyze and estimate the parameters of our objective function using Eviews.

Spatial Differentiation Characteristics and Driving Forces of Forest Transition:A Case Study of Zunyi City,Guizhou

Spatial differentiation in forest transition was measured in terms of space transition and function transition using the exploratory spatial data analysis method（ESDA） and data from 2004—2014 for Zunyi city,Guizhou province,China.The validity of factors affecting forest transition was analyzed by constructing radial basis function neural networks（RBFNN） based on the data processing system（DPS）.Our results will provide references for scientific understanding of the potential mechanism underlying forest transition in mountainous areas.We found that Global Moran＇s I of space transition and function transition of forest land was 0.0336 and 0.2323,respectively.This suggests a significant positive correlation in spatial distribution of space transition and function transition of forest land,and significant spatial aggregation.The Global Moran＇s I of function transition was higher than that of space transition,and the spatial aggregation characteristics of function transition were more significant than for space transition.The Global Moran＇s I at each time period tended to increase,and the spatial aggregation degree of the function transition and space transition was further enhanced.Hot and cold spots of space transition of forest land stably evolved,suggesting a minor spatial difference in forest land among different administrative units at the county level.The number of hot spots at the county level in function transition increased.Hot spots were intensively distributed at the western edge and continuously distributed in the northeast.The space transition and function transition of forest land were both greatly influenced by urbanization rate and second and third industries.The development of urbanization and industrialization was the main factor driving forest transition,suggesting a positive role of economic growth on forest transition in mountainous areas.The development of urbanization and industrialization is an effective approach to forest transition in mountainous areas.

Geographical patterns and anti-poverty targeting post-2020 in China

Poverty has been a focus of Chinese government for a long time. It is therefore of great significance to investigate both the mechanisms and spatial patterns of regional impoverishment in order to adequately target Chinese anti-poverty goals. Based on the human-environment relationship and multidimensional poverty theory, this study initially develops a three-dimensional model encompassing human, society, and environmental factors to investigate the mechanisms of rural impoverishment as well as to construct an indicator system to evaluate the comprehensive poverty level(CPL) in rural areas. A back propagation neural network model was then applied to measure CPL, and standard deviation classification was used to identify counties that still require national policy-support(CRNPSs) subsequent to 2020. The results of this study suggest that CPL values conform to a decreasing trend from the southeast coast towards the inland northwest of China. Data also show that 716 CRNPSs will be present after 2020, mainly distributed in high-arid areas of the Tibetan Plateau, the transitional zones of the three-gradient terrain, as well as karst areas of southwest China. Furthermore, CRNPSs can be divided into four types, that is, key aiding counties restricted by multidimensional factors, aiding counties restricted by human development ability, aiding counties restricted by both natural resource endowment and socioeconomic development level, and aiding counties restricted by both human development ability and socioeconomic development level. We therefore propose that China should develop and adopt scientific and targeted strategies to relieve the relative poverty that still exist subsequent to 2020.

Self-contained eigenvector algorithm applied to the identification of aerodynamic derivatives of bridge model

On one hand, when the bridge stays in a windy environment, the aerodynamic power would reduce it to act as a non-classic system. Consequently, the transposition of the system’s right eigenmatrix will not equal its left eigenmatrix any longer. On the other hand, eigenmatrix plays an important role in model identification, which is the basis of the identification of aerodynamic derivatives. In this study, we follow Scanlan’s simple bridge model and utilize the information provided by the left and right eigenmatrixes to structure a self-contained eigenvector algorithm in the frequency domain. For the purpose of fitting more accurate transfer function, the study adopts the combined sine-wave stimulation method in the numerical simulation. And from the simulation results, we can conclude that the derivatives identified by the self-contained eigenvector algorithm are more dependable.