Analytic models for West Coast storm surges,with application to events of January 1976

Simple mathematical models describing wind-induced motion on a continental shelf are used to predict storm surges at the port of Milford Haven on the west coast of the British Isles. Attention is concentrated on surge events occurring during the stormy month of January 1976. It is demonstrated that useful first-order accuracies in prediction can be achieved. Oceanic influence on the coastal surge is shown to be small. Satisfactory results are obtained with wind input data coming either from the analysis of daily weather charts or from recorded values of wind speed and direction at a group of meteorological stations.     

Analysing production and environmental risks in arable farming systems: A mathematical approach

In response to a growing environmental concern in Dutch society, sustainable production systems in arable farming have been developed. Amongst other things, a reduction of the dependency on chemical inputs is attempted. This paper addresses the role of risk in the adoption by farmers of new systems by means of a model that determines differences in production risks between conventional and sustainable farming systems (CAFS and IAFS).Timing of activities – setting out a management track – is particularly important in sustainable arable farming systems. Resource requirements of crop husbandry activities mainly depend on weather conditions. To assess risks caused by weather conditions, the major aspects of crop husbandry in various crops have been modelled. Using tactics in crop husbandry (decision rules) and weather uncertainty as input, crop husbandry models (HMs) calculate management tracks that require resources. The value distributions of resource requirements of crop husbandry according to different farming systems is calculated in different HMs represented by stochastic dynamic directed networks. Hence, production risks of CAFS and IAFS can be compared.On a farm, all the aspects of crop husbandry in the various crops are to be taken into account. Given the weather conditions, tactics for all the aspects are combined in an LP model of the whole farm where they compete for limited resources. In the LP model, tactics are re-assessed by means of the HMS, using information of the LP solution. This iterative procedure enables production risks of CAFS and IAFS to be compared, considering fixed, allocatable resources for the whole farm firm.     

Univariate and bivariate GPD methods for predicting extreme wind storm losses

Wind storm and hurricane risks are attracting increased attention as a result of recent catastrophic events. The aim of this paper is to select, tailor, and develop extreme value methods for use in wind storm insurance. The methods are applied to the 1982-2005 losses for the largest Swedish insurance company, the Länsförsäkringar group. Both a univariate and a new bivariate Generalized Pareto Distribution (GPD) gave models which fitted the data well. The bivariate model led to lower estimates of risk, except for extreme cases, but taking statistical uncertainty into account the two models lead to qualitatively similar results. We believe that the bivariate model provided the most realistic picture of the real uncertainties. It additionally made it possible to explore the effects of changes in the insurance portfolio, and showed that loss distributions are rather insensitive to portfolio changes. We found a small trend in the sizes of small individual claims, but no other trends. Finally, we believe that companies should develop systematic ways of thinking about “not yet seen” disasters.     

The cost of weather in a floating oil production system

New oil fields are being developed in deeper water where conventional production systems are impractical. One alternative is the floating production, storage and offloading system: oil is extracted and stored on a moored, floating tanker while a shuttle tanker transports the oil between the field and the refinery port. However, when the weather is too rough, mooring and offloading has to be suspended. If there is inadequate storage capacity, oil production also stops, resulting in a costly interruption to the revenue flow. Simulation experiments with different design configurations can identify the economic optimum that minimises the financial impact of the weather on the operation. However, not all of the uncertainties can be captured completely in a quantitative manner and sensitivity analyses suggest that a more robust configuration is a better option than the simple optimum.     

缝洞型碳酸盐岩油藏注水驱油提高采收率研究

Y油田为缝洞型碳酸盐岩油藏,油藏复杂的地质特征决定了其开发模式有别于常规砂岩油藏.为了有效提高Y油田采收率,利用Eclipse数值模拟软件建立了地质模型,对水平井长度、射孔间距及酸压缝长等油藏产能影响因素与转注时机、注采比以及关井时间等注采参数进行了优化.结果认为:对于Y缝洞型碳酸盐岩油藏,水平井水平段长度为500m、射孔间距为20m或酸压规模为100m时,累计产油量可达到最大值;压力降至废弃压力时进行转注,可获得最大采收率;周期注采比等于1时,周期时效与累计增油量达到最大值;关井时间为8天时,采收率提高幅度最大.     

A new approach in modelling undesirable output in DEA model

With an increasing attention on the environment, one of the major research thrusts in Data Envelopment Analysis (DEA) based performance evaluation is the undesirable output in the conventional DEA model. There is considerable research published on the undesirable aspects of production outputs. However, the economic implications and the suitability of the DEA models for incorporating the undesirable outputs are less carefully investigated and discussed. In this paper, a comparative study is conducted of typical eco-DEA models to illustrate this issue. We propose a ratio model to evaluate the undesirable as well as the desirable outputs simultaneously. We apply the specially developed model to investigate the impact of production pollutants while conducting the efficiency evaluation in the textile industry of China. The results reveal that the production output-oriented efficiency evaluation can be significantly altered once the environmental aspects are factored into the model.     

Weather Risk in Offshore Projects

Risk analysis tools have been used to help manage various projects. This paper describes a case study in which an extension to the stochastic project network model was developed for a risk analysis of an oil platform installation, quantifying the possible impact of the weather on the project's schedule. Examination of the weather data suggested the use of a Markov weather model combined with a separate residence time distribution for key states. The weather model was incorporated into the stochastic project network allowing the interactions of the various project uncertainties to be examined. While the weather introduced a significant additional risk to the project, analysis of management's options indicated that much of the risk might be avoided. In particular, the analysis quantified the benefits of scheduling the project start to take advantage of the seasonal variations and hiring heavy duty equipment to operate in more arduous conditions.     

Numerical Stochastic Model of Non-stationary Time Series of the Wind Chill Index

A numerical stochastic model of the high-resolution time series of the wind chill index is considered. The model is constructed under the assumption that time series of the wind chill index are non-stationary non-Gaussian random processes with time-dependent one-dimensional distributions. This assumption makes possible to take into account both daily and seasonal variations of real meteorological processes. Data of the long-term real observations at weather stations were used for estimating the model parameters and for the verification of the model. Based on the simulated trajectories, some statistical properties of rare and adverse weather events, like long periods of time with a low wind chill index, are studied. The model is also used to study the dependence of the statistical properties of the wind chill index time series on a climate change.

     

A comparison of regime-switching temperature modeling approaches for applications in weather derivatives

A comparison of regime-switching approaches to modeling the stochastic behavior of temperature with an aim to the valuation of temperature-based weather options is presented. Four models are developed. Three of these are two-state Markov regime-switching models and the other is a single-regime model. The regime-switching models are generated from a combination of different underlying processes for the stochastic component of temperature. In Model 1, one regime is governed by a mean-reverting process and the other by a Brownian motion. In Model 2, each regime is governed by a Brownian motion. In Model 3, each regime is governed by a mean-reverting process in which the mean and speed of the mean-reversion remain the same, but only the volatility switches between the states. Model 4 is a single-regime model, where the temperature dynamics are governed by a single mean-reverting process. All four models are utilized to determine the expected heating degree days (HDD) and cooling degree days (CDD), which play a crucial role in the valuation of weather options. A four-year temperature dataset from Toronto, Canada, is used for the analysis. Results demonstrate that Model 1 captures the temperature dynamics more accurately than the other three models. Model 1 is then used to price the monthly call options based on a range of strike HDD.     

Integration of Markov and Linear Programming Models to Assess the Farmgate and National Consequences of Adopting New Bovine Reproductive Technologies in the United Kingdom Agriculture

Despite many advances in biotechnology being futuristic, in some industries such technological improvements are now becoming generally available for absorption into commercial practice. The new reproductive technologies for bovine animals are such an example. For a regulated industry like agriculture, an ex ante evaluation of the impact of their adoption is the concern of policy makers. This paper describes a modelling strategy devised to assess these impacts, using a sequence of stochastic, financial and linear programming models. A set of individual farm type models were developed and later extended to a national model to simulate the interactions that occur between dairy and beef enterprises and the competition between them for resources on individual farms. Restraints reflecting national policies such as quotas on milk production and industry support through subsidies are built into the model to explore several scenarios. These models are used to assess the impact of new technologies on dairy and beef production nationally and for regions which have been identified by the Ministry of Agriculture, Fisheries and Food.     

基于改进型GERT网络的供应链突发事件风险传递研究

突发事件会对供应链运作产生巨大影响,甚至会导致整个网络断裂。针对供应链系统中突发事件的风险传递问题,分别构建了基于GERT网络的供应链正常交付模型、延期交付模型和突发事件风险传递模型,并依据模型结构,推导了相应的矩母函数和传递函数表达式。在模型解析的基础上,设计了供应链突发事件风险传递的定量化分析策略。通过对比供应链正常交付模型和延期交付模型的求解结果,得到各个环节的最终延迟时间;再通过突发事件风险传递模型的拟合计算,进一步定量化描述风险在逐级传递过程中的叠加程度。最后,通过一个汽车供应链网络案例,验证了模型和方法的适用性。     

Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms

Pesticides’ dynamic effects and production uncertainty play an important role in farmers’ production decisions. Pesticides have a current production impact through reducing crop damage in the current period and a future impact through impacting the farm biodiversity which alters the future production environment. This study presents the difference in inefficiency arising from models that ignore the dynamic effects of pesticides in production decisions and the impact of production uncertainty. A dynamic data envelopment analysis (DEA) model is applied to outputs, inputs, and undesirables of Dutch arable farms over the period 2003–2007. A bootstrap approach is used to explain farmers’ performance, providing empirical representations of the impact of stochastic elements on production. These empirical representations are used to adjust firms’ inefficiency scores to incorporate production uncertainty in efficiency evaluation. We find that efficiency increased dramatically when a production technology representation that considers both pesticides’ dynamic impacts, and production uncertainty is adopted.     

CME on March 16, 2001, electron pulsation event and solar-terrestrial phenomena related with CMEs

The electron pulsation event is defined in the paper. Firstly, a slow Halo CME on March 16, 2001 that led to low-energetic solar proton event, electron pulsation event and major geomagnetic storm was analyzed. And then, dozens of events are collected. The interrelations among the solar flare, CME, solar proton event, electron pulsation event and geomagnetic storm are studied. The results show that: (i) Solar proton events can be regarded as the indication that CMEs get to the earth and the electron pulsation events can be regarded as the indication of solar proton flux. (ii) Not only can fast CMEs strongly influence the earth, but also slow CMEs can influence the earth, and its influences are more frequent and cannot be neglected. (iii) Most of high-energetic solar proton events withE≥10 MeV can lead to geomagnetic storms, but most of the medium and weak geomagnetic storms result from low-energetic solar proton events that are caused by CMEs. (iv) Both the electron pulsation events and geomagnetic storms are the link effects of high- and low-energetic solar proton events, but the occurrence of electron pulsation event are generally prior to the geomagnetic storm. So in the circumstance where the near real-time observing data of the low-energetic solar proton event cannot be obtained, we can regard electron pulsation event as the indication of the low-energetic solar proton flux reaching the earth, which can be used as one of the important references of short-term prediction and alert of the geomagnetic storm.     

Machine-learning classifiers for imbalanced tornado data

Learning from imbalanced data, where the number of observations in one class is significantly larger than the ones in the other class, has gained considerable attention in the machine learning community. Assuming the difficulty in predicting each class is similar, most standard classifiers will tend to predict the majority class well. This study applies tornado data that are highly imbalanced, as they are rare events. The severe weather data used herein have thunderstorm circulations (mesocyclones) that produce tornadoes in approximately 6.7 % of the total number of observations. However, since tornadoes are high impact weather events, it is important to predict the minority class with high accuracy. In this study, we apply support vector machines (SVMs) and logistic regression with and without a midpoint threshold adjustment on the probabilistic outputs, random forest, and rotation forest for tornado prediction. Feature selection with SVM-recursive feature elimination was also performed to identify the most important features or variables for predicting tornadoes. The results showed that the threshold adjustment on SVMs provided better performance compared to other classifiers.     

Quality improvement and setup reduction in the joint economic lot size model

The co-maker concept has become accepted practice in many successful global business organizations. This fact has resulted in a class of inventory models known as joint economic lot size (JELS) models. Heretofore such models assumed perfect quality production on the part of the vendor. This paper relaxes this assumption and proposes a quality-adjusted JELS model. In addition, classical optimization methods are used to derive models for the cases of setup cost reduction, quality improvement, and simultaneous setup cost reduction and quality improvement for the quality-adjusted JELS. Numerical results are presented for each of these models. Comparisons are made to the basic quality-adjusted model. Results indicate that all three policies exhibit significantly reduced total cost. However, the simultaneous model results in the lowest cost overall and the smallest lot size. This suggests a synergistic impact of continuous improvement programs that focus on both setup and quality improvement of the vendor's production process. Sensitivity analysis indicates that the simultaneous model is robust and representative of practice.     

中国西北地区沙尘暴的发生规律研究

借助MATLAB软件,根据中国西北地区六个省份1981～2005年沙尘暴发生天数、月平均风速、大风发生天数、月平均气温、月降水量、月平均相对湿度的统计数据,研究中国西北地区沙尘暴的区域分布和时间分布规律及其与气象因子的关系.方差分析结果表明:西北地区沙尘暴危害程度省份差异不显著,不同观测站所在地沙尘暴危害程度的差异显著,不同月份沙尘暴危害程度的差异也显著;聚类分析结果表明:拐子湖等站点所在地受沙尘暴危害最严重,各年4月份受沙尘暴危害最严重,北京地区8月份几乎不发生沙尘暴;相关分析结果表明:西北地区沙尘暴的气候影响因子为月平均风速、月大风发生天数和月相对湿度.由回归分析得到,沙尘暴每月发生天数y随月份t的变化规律为正态分布.研究结果可供有关部门参考.     

Multiple ground-based observations at Zhongshan Station during the april/may 1998 solar events

Simultaneous observations at Zhongshan Station, Antarctica, during May 1–7, 1998 are presented to show the responses of the polar ionosphere to the April/May 1998 solar events. One of the main geo-effects of the solar events resulted in the major magnetic storm on May 4. At the storm onset on May 2 the ionosphere F2 layer abruptly increased in altitude, the geomagnetic H-component started negative deviation and the spectral amplitude of the ULF wave intensified. Both large isolated riometer absorption and large negative deviation of the geomagnetic H-component occurred at about 0639UT. There was a time lag of about one hour and ten minutes between the storm onset and the IMF southward turning, as measured by the WIND satellite. The polar ionosphere was highly disturbed, as shown by frequent large deviations of the geomagnetic H-component, large riometer absorption events and strong ULF waves in all the courses of the storm. The absorption increased greatly causing the digisonde to be blackout most of the time. However, the data still showed a substantial decrease in the F2 electron density and oscillation of the F2 layer peak height with an amplitude exceeding 200 km.

     

CME on March 16, 2001, electron pulsation event and solar-terrestrial phenomena related with CMEs

The electron pulsation event is defined in the paper. Firstly, a slow Halo CME on March 16, 2001 that led to low-energetic solar proton event, electron pulsation event and major geomagnetic storm was analyzed. And then, dozens of events are collected. The interrelations among the solar flare, CME, solar proton event, electron pulsation event and geomagnetic storm are studied. The results show that: (ⅰ) Solar proton events can be regarded as the indication that CMEs get to the earth and the electron pulsation events can be regarded as the indication of solar proton flux. (ⅱ) Not only can fast CMEs strongly influence the earth, but also slow CMEs can influence the earth, and its influences are more frequent and cannot be neglected. (ⅲ) Most of high-energetic solar proton events with E≥10 MeV can lead to geomagnetic storms, but most of the medium and weak geomagnetic storms result from low-energetic solar proton events that are caused by CMEs. (ⅳ) Both the electron pulsation events and geomagnetic storms are the link effects of high- and low-energetic solar proton events, but the occurrence of electron pulsation event are generally prior to the geomagnetic storm. So in the circumstance where the near real-time observing data of the low-energetic solar proton event cannot be obtained, we can regard electron pulsation event as the indication of the low-energetic solar proton flux reaching the earth, which can be used as one of the important 参考文献 of short-term prediction and alert of the geomagnetic storm.     

Storm processes and stochastic geometry

This paper is devoted to a prototype of max-stable models called the storm process. At first its spatial distribution is given in association with different observation supports. Then the compatibility relationships between extremal coefficients at various supports are completely characterized. Particular attention is paid to the special case where the storms are indicator functions of Poisson polytopes. Explicit formulae are found for the extremal coefficients with finite or convex supports. A new algorithm for exactly simulating the Poisson storm process in continuous space is also provided. Overall, the storm process can be used as a benchmark for comparing the performances of several estimators of extremal coefficients, or for model selection.