Investigation of boundary‐layer wind predictions during nocturnal low‐level jet events using the Weather Research and Forecasting model

The accuracy of boundary‐layer wind profiles occurring during nocturnal low‐level jet (LLJ) events, and their sensitivities to variations of user‐specifiable model configuration parameters within the Weather Research and Forecasting model, was investigated. Simulations were compared against data from a wind‐profiling lidar, deployed to the Northern Great Plains during the U.S. Department of Energy‐supported Weather Forecast Improvement Project. Two periods during the autumn of 2011 featuring LLJs of similar magnitudes and durations occurring during several consecutive nights were selected for analysis. Simulated wind speed and direction at 80 and 180 m above the surface, the former a typical wind turbine hub height, bulk vertical gradients between 40 and 120 m, a typical rotor span, and the maximum wind speeds occurring at 80 and 180 m, and their times of occurrence, were compared with the observations. Sensitivities of these parameters to the horizontal and vertical grid spacing, planetary boundary layer and land surface model physics options, and atmospheric forcing dataset, were assessed using ensembles encompassing changes of each of these configuration parameters. Each simulation captured the diurnal cycle of wind speed and stratification, producing LLJs during each overnight period; however, large discrepancies in relation to the observations were frequently observed, with each ensemble producing a wide range of distributions, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error and bias values computed over the LLJ cycle (late evening through the following morning) revealed that, while some configurations performed better or worse in different aspects and at different times, none exhibited definitively superior performance. The considerable root mean square error and bias values, even among the ‘best’ performing simulations, underscore the need for improved simulation capabilities for the prediction of near‐surface winds during LLJ conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

All-zero forecasts for lumpy demand: a factorial study

Lumpy demand is a phenomenon encountered in manufacturing or retailing when the items are slow-moving or too expensive, for example fighter plane engines. So far, the seminal procedure of Croston's (1972 Croston, JD. 1972. Stock levels for slow-moving items. Op. Res. Q., 25(1): 123–130. [Taylor &; Francis Online] , [Google Scholar]), with or without modifications, has been the preference for forecasting lumpy demand. Nevertheless, Croston (1974 Croston, JD. 1974. Forecasting and stock control for intermittent demand. Op. Res. Q., 23(3): 289–303. [Taylor &; Francis Online] , [Google Scholar]) and others, such as Venkitachalam et al . (2002 Venkitachalam, GHK, Pratt, DB, Young, CF, Morris, S and Goldstein, ML. 2002. Forecasting and inventory planning for parts with intermittent demand–a case study [http://anaconda.ecen.okstate.edu/publications/ERC2003-ADF-Final.doc] [Google Scholar]), have suggested the use of zero forecasts when the demand contains many zeros. In this paper, we put to the test this idea by doing a full factorial study comparing five forecasting methods, including all-zero, under several levels of demand lumpiness, demand variation and ordering, holding and shortage cost. We evaluate the forecasting methods by three measures of forecast error and two measures of inventory cost. We find that all-zero forecasts yield the lowest cost when lumpiness is high; is it also best for mid-lumpiness, if the shortage cost is much higher than the holding cost. We also find that the lowest forecasting error does not necessarily lead to the lowest system cost. And contrary to the assertions in Chen et al . (2000b Chen, F, Ryan, JK and Simchi-Levi, D. 2000b. The impact of exponential smoothing forecasts on the bullwhip effect. Naval Research Logistics, 42: 269–286.  [Google Scholar]) and Dejonckheere et al . (2003 Dejonckheere, J, Disney, SM, Lambrecht, MR and Towill, DR. 2003. Measuring the Bullwhip Effect: a Control Theoretic Approach to Analyse Forecasting Induced Bullwhip in Order-Up-To Policies. Eur. J. Op. Res., 147(3): 567–590. [Crossref], [Web of Science ®] , [Google Scholar], 2004 Dejonckheere, J, Disney, SM, Lambrecht, MR and Towill, DR. 2004. The impact of information enrichment on the bullwhip effect in supply chains: a control theoretic approach. Eur. J. Op. Res., 153(3): 727–750. [Crossref], [Web of Science ®] , [Google Scholar]), our factorial experiment reinforces the intuition that simple exponential smoothing is superior to an equivalent moving average.     

Forecasting Trending Time Series with Relative Growth Rate Models

Many annual time series in socioeconomic systems are steadily increasing functions of time. This paper deals with an empirical approach to analyzing and projecting such trending time series from models of relative growth rates or percent changes. A class of relative growth rate models is defined which includes the linear. exponential, modified exponential and logistic growth curves as special eases. Parameters are estimated for the most part by linear regression techniques since the, relative growth rates for this class of models are linear in the parameters. The ternd curve for each model is obtained by integration and approximate-confidence limits can be obtained for the forecasts of future series values.     

LD2铝合金加速腐蚀蚀坑演化的ARIMA模型研究

依据加速腐蚀环境谱进行飞机LD2结构试件的加速腐蚀试验,并将LD2材料的加速腐蚀产生及发展过程视为随机过程,提出基于时间序列理论进行腐蚀损伤预测研究的方法,建立描述LD2材料在加速腐蚀环境下蚀坑深度演化规律的时间序列ARIMA(3,1,1)模型,并利用模型进行腐蚀深度预测研究。结果表明,所建的ARIMA(autoregressive integrated moving average,求和自回归移动平均)模型能以较高的精度预测未来一段周期内LD2材料蚀坑深度的发展趋势,说明采用基于时间序列理论的ARIMA模型方法进行飞机LD2结构材料腐蚀损伤预测研究有效可行。     

小批量产品生产过程统计质量控制模型研究

在分析研究现有小批量统计质量控制相关理论的基础上,基于贝叶斯原理,提出了一种综合解决小批量生产过程质量控制的建模方法,此方法在保证第一类错误较小的前提下有效地减小第二类错误的发生,即在误发警报率尽量小的前提下减小漏发警报的发生。     

海产贝类食品安全预警研究

为防控海贝类食源性污染、食物中毒的发生,通过开展贝类生物海洋污染本底调查、进行贝类食用安全性分析评价、在海区布设预警站点、实施贝毒素预警监测、建设海贝类食品安全示范基地等方式,探索出一条适合当地沿海实际的贝类食品安全预警保障模式,并对贝类食品依法行政、贝类的清洁生产与健康养殖等提出了见解。     

煤粮复合区耕地演变预测信息系统的构建

为了满足管理部门对煤矿区耕地管理的需要,设计了该系统。论述了煤粮复合区的研究背景和意义,分析了现有的采煤沉陷预测系统的不足,对煤粮复合区耕地演变预测系统的总体结构、功能、数据库、接口、安全保障体系等进行了阐述。该系统功能强大,能够实现煤粮复合区耕地变化的预测和管理,为管理部门制订管理决策提供科学依据。     

基于电流信号钻头磨损状态预报系统

介绍了一个钻头磨损状态在线预报系统。通过监测主电机电流信号建立神经网络动态预报模型 ,对钻头后刀面磨损量分类建立在不同磨损类别下的神经网络模型 ,以神经网络模型估算的电流值作为模糊聚类中心 ,根据预报电流值对钻头磨损状态进行模糊分类 ,从而预报磨损状态。实验表明 ,此预报系统具有较高的成功率和可靠性     

Multivariate performance reliability prediction in real-time

This paper presents a technique for predicting system performance reliability in real-time considering multiple failure modes. The technique includes on-line multivariate monitoring and forecasting of selected performance measures and conditional performance reliability estimates. The performance measures across time are treated as a multivariate time series. A state–space approach is used to model the multivariate time series. Recursive forecasting is performed by adopting Kalman filtering. The predicted mean vectors and covariance matrix of performance measures are used for the assessment of system survival/reliability with respect to the conditional performance reliability. The technique and modeling protocol discussed in this paper provide a means to forecast and evaluate the performance of an individual system in a dynamic environment in real-time. The paper also presents an example to demonstrate the technique.     

国内外深加工钼制品概况

对国内外深加工钼制品现状、发展趋势作了阐述,并对2005年深加工钼制品的情况作了预测,还对我国深加工钼制品今后的发展提出了建议。