基于风云3C卫星双氧通道的云检测算法

风云3C卫星微波湿度计(MWHS)增加一组频点为118.75 GHz的氧气吸收通道,该组探测通道与微波湿度计(MWTS)中的50~60 GHz的氧气吸收通道综合发展一种云检测方法。应用高频和低频通道对云的散射作用不同的原理,利用在根据双氧通道之间在晴空条件下的关系,进而发展出的一种综合的云检测方法。     

The effects of the non-breaking surface wave-induced vertical mixing on the forecast of tropical cyclone tracks

The Weather Research and Forecasting model(WRF)is configured for the region of(15°–41°N,105°–135°E),which covers the same area with the MASNUM(Key Lab.Marine ScienceNumerical Modeling,State Oceanic Administration)wave-tide-circulation coupled operational forecast system.Three numerical experiments are implemented to investigate the effects of the real-time forecasted sea surface temperature(SST)and the nonbreaking wave-induced vertical mixing(Bv)on the track forecast of all 33 tropical cyclones(TC)in the model domain area during 2008 and 2011.The first experiment employs NCEP FNL(NCEP final analysis)SST as WRF’s bottom condition as the Control run,which is also the default setup of WRF.The second and third experiments use real-time forecasted SST from the MASNUM forecast system with and without Bv,respectively.The forecasted track results are compared with Japan Meteorological Agency’s best track data.For 24-h forecast,the averaged TC position error of Experiment with Bv is reduced by 9%compared to the Control experiment,while the forecasted track error of Experiment without Bv is reduced by only2%compared to the Control experiment.For the 48-h forecast,the averaged track errors are reduced by 10%and6%with Bv and without Bv compared to the Control experiment,respectively.These results suggest that the real-time forecasted SST can improve the performance of WRF in forecasting TC track,and the Bv plays an important role in reducing the forecast error of TC track.Comparatively,Bv can improve more on the track of stronger TC.     

函件业务量的多元回归预测方法

函件业务是邮政的核心和基础业务，函件业务的预测对制定邮政企业发展规划具有十分重要的意义。首先介绍了影响函件业务量的主要因素，然后给出了建立函件业务量多元回归模型的方法，并用近年来的有关数据建立了函件业务量实际模型，对近三年的函件业务量进行了预测。     

Correspondence on the selection of error measures for comparisons among forecasting methods

Clements and Hendry (1993) proposed the Generalized Forecast Error Second Moment (GFESM) as an improvement to the Mean Square Error in comparing forecasting performance across data series. They based their conclusion on the fact that rankings based on GFESM remain unaltered if the series are linearly transformed. In this paper, we argue that this evaluation ignores other important criteria. Also, their conclusions were illustrated by a simulation study whose relationship to real data was not obvious. Thirdly, prior empirical studies show that the mean square error is an inappropriate measure to serve as a basis for comparison. This undermines the claims made for the GFESM.     

家具销售中预测模型的研究

用灰色非线性建模理论，建立了家具销售方面的两个预测模型，为家具企业的生产、销售，提供了一个科学而又容易操作的决策方法。     

大豆根潜蝇预测预报方法的研究

在多年研究的基础上，查阅国内文献，针对大豆根潜蝇的田间调查、发生期预测、发生量预测及防治指标等问题进行了系统的阐述．对我国大豆根潜蝇的预测预报及综合防治工作具有指导作用和实用性。     

经常购买模型及其参数的极大似然估计

在分析巴斯模型和重复购买的产品生命周期模型等的基础上，提出经常购买模型，详细讨论该模型的参数的极大似然估计法，并采用实例检验模型及参数估计方法，分析模型的参数估计结果，在此基础上使用模型做产品销量预测，表明模型的参数估计方法及预测方法的有效性。     

Minimum variance pooling of forecasts at different levels of aggregation

The most common approach to combining forecasts at different levels of aggregation has been to sum (or average) the more disaggregated forecast, and take a weighted average of the aggregate forecasts. This paper develops a simple method for obtaining minimum variance pooled forecasts at the disaggregated level. The major advantage that this method has over the common approach is that it provides pooled forecasts at both the aggregated and disaggregated level. As will be shown, the resulting aggregate pooled forecast is identical to the forecast which would be obtained by simply pooling two forecasts at the aggregate level, while the disaggregated forecast maintains the aggregation identity required by the problem.     

Time-series forecasting of the German unemployment rate

The purpose of the paper is to investigate the accuracy of forecasts derived from univariate and multivariate time-series models. An iterative method to adjust for impact assessment in univariate ARIMA models is discussed and illustrated for the German unemployment rate. Finally, we also examine the pros and cons of the impact assessment model in comparison with VAR models.     

Forecast evaluation and improvement using theil's decomposition

Forecasters are concerned with the accuracy of a forecast and whether the forecast can be modified to yield an improved performance. Theil has proposed statistics to measure forecast performance and to identify components of forecast error. However, the most commonly used of Theil's statistics have been shown to have serious shortcomings. This paper discusses Theil's decomposition of forecast error into bias, regression and disturbance proportions. Examples using price expectations and new housing starts data are given to show how decomposition suggests a linear correction procedure that may improve forecast accuracy.