Regional Temperature Forecast for the Next Day in Hong Kong

For a century or so, the Hong Kong Observatory (HKO) has been providing temperature forecast for the whole of Hong Kong with the HKO Headquarters as the reference location. In recent decades, due to spreading of population from the main urban center to satellite towns, there is an increasing demand for regional temperature forecasts. To support such provision, the HKO has developed a regression model to provide objective guidance to forecasters in formulating forecasts of maximum and minimum temperatures for the next day at various locations in Hong Kong. In this paper, the regression model is presented, together with the assessment of its performance. Based on the verification of one year of forecasts, it is found that the root mean square errors (RMSEs) of maximum (minimum) temperature forecasts are from about 1.3 to 2.1 (1.1 to 1.4) degrees, respectively. The regression model is shown to have generally out-performed the operational regional spectral model then operated by HKO. Regional temperature forecast methods of other meteorological or research centers are also surveyed. Equipped with the regression model, the HKO has launched an online regional temperature forecast service for the next day in Hong Kong since March 2008.     

基于MonoRTM模型的微波辐射计反演方法研究

基于辐射传输模型MonoRTM计算天空亮温度,使用多元线性统计回归方法和BP神经网络方法分别对大气温度和水汽密度廓线进行了反演,检验并分析了两种方法的反演精度.结果表明,多元线性回归方法反演的温度偏差总体不大于6K,反演的水汽密度偏差小于4 g/m3;神经网络方法反演的温度偏差小于2K,反演的水汽密度误差总体不大于2 g/m3.与探空数据的对比表明,对于大气温度和水汽密度反演,BP神经网络方法的反演结果都要比多元线性回归方法的反演结果更接近探空资料值.     

基于遥感的土壤电阻率估算研究

以我国长江中下游的南京及其周边15个市(县)为例,采用野外测定、室内分析与遥感反演相结合的方法,开展了土壤电阻率估算研究.选用影响土壤电阻率的土壤水分、土壤温度、土壤可溶盐总量与土壤阳离子交换量等4个主要因子,遥感反演土壤水分和温度空间分布,以获取估算土壤电阻率所需要的主要参数;采用偏最小二乘二次多项式(PLSQM)模型对不同地表覆盖类型下的土壤电阻率进行估算,PLSQM估算模型的估算值与实测值的相关系数达到0.85,平均相对误差(MRE)为19.02%,均方根误差(RMSE)为7.79.结果表明,草地、农田、林地3种不同地表覆盖类型下土壤电阻率有明显差异,PLSQM模型实现了较高估算精度,具有较好的应用潜力.     

A simple gravity wave drag parametrization for use in medium‐range weather forecast models

Abstract

A simple gravity wave drag parametriiation over mountainous terrain is tested for its ability to reduce the systematic errors of medium‐range weather forecasts. Following Boer et al. (1984), this parametrization is a function of the low‐level wind speed and stability, the local Froude number, and the variance of the subgrid‐scale orographie features.

A comparison study of ten 7‐day forecasts obtained with envelope orography, wave drag or standard orography, shows that wave drag is as effective as envelope orography in reducing the systematic errors. A further comparison where the combined effects of the wave drag and that of a complementary enhanced orography (that is one that includes only the subgrid‐scale elements not treated separately by wave drag) are taken into account shows this latter approach to be the most promising in reducing orographically‐related systematic errors.     

公众气象服务支付意愿影响因素研究

基于中国气象局2008年调查数据,运用二分Logistic回归、最优尺度回归和判定树CRT模型分析了公众气象服务支付意愿的影响因素。结果表明：个体因素中年龄与文化程度对公众气象服务支付意愿（包括是否支付与支付多少）有显著影响,而性别因素只对支付多少有显著影响,对是否支付没有显著影响;收入因素对公众气象服务支付意愿有显著影响;关注程度、天气预报准确性、天气预报及时性、气象服务总体满意度对公众气象服务支付数额多少有显著影响,但影响程度很小;个体因素与收入因素对公众气象服务支付意愿影响程度较高,而心理因素影响程度很小。     

半参数模型统计诊断与粗差检验

This paper systematically studies the statistical diagnosis and hypothesis testing for the semiparametric linear regression model according to the theories and methods of the statistical diagnosis and hypothesis testing for parametric regression model.Several diagnostic measures and the methods for gross error testing are derived.Especially,the global and local influence analysis of the gross error on the parameter X and the nonparameter s are discussed in detail;at the same time,the paper proves that the d...     

Estimating sagebrush cover in semi-arid environments using Landsat Thematic Mapper data

Sagebrush ecosystems of the western US provide important habitat for several ungulate and vertebrate species. As a consequence of energy development, these ecosystems in Wyoming have been subjected to a variety of anthropogenic disturbances. Land managers require methodology that will allow them to consistently catalog sagebrush ecosystems and evaluate potential impact of proposed anthropogenic activities. This study addresses the utility of remotely sensed and ancillary geospatial data to estimate sagebrush cover using ordinal logistic regression. We demonstrate statistically significant prediction of ordinal sagebrush cover categories using spectral (χ² = 113; p < 0.0001) and transformed indices (χ² = 117; p < 0.0001). Both Landsat spectral bands (c-value = 0.88) and transformed indices (c-value = 0.89) can distinguish sites with closed, moderate and open cover sagebrush cover categories from no cover. The techniques described in this study can be used for estimating categories of sagebrush cover in arid ecosystems.     

Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests

In this paper, we compare and contrast a Bayesian spatially varying coefficient process (SVCP) model with a geographically weighted regression (GWR) model for the estimation of the potentially spatially varying regression effects of alcohol outlets and illegal drug activity on violent crime in Houston, Texas. In addition, we focus on the inherent coefficient shrinkage properties of the Bayesian SVCP model as a way to address increased coefficient variance that follows from collinearity in GWR models. We outline the advantages of the Bayesian model in terms of reducing inflated coefficient variance, enhanced model flexibility, and more formal measuring of model uncertainty for prediction. We find spatially varying effects for alcohol outlets and drug violations, but the amount of variation depends on the type of model used. For the Bayesian model, this variation is controllable through the amount of prior influence placed on the variance of the coefficients. For example, the spatial pattern of coefficients is similar for the GWR and Bayesian models when a relatively large prior variance is used in the Bayesian model.      

B样条与磨光样条在基于矩形格网的DEM内插中的应用

本文研究了Ｂ样条和磨光样条应用在基于矩形格网的ＤＥＭ内插中的思想、方法及实现过程。并从逼真性,保凸性,光滑性,运算速度等方面对Ｂ样条、磨光样条与双线性内插算法做了实验比较。理论和实验表明,所述方法具有较好的效果。     

郑徐高铁开封段地面沉降分层监测方法

高铁沿线施工期间引起地面沉降的因素错综复杂、相互影响,本文提出通过对沉降区域地下水位、孔隙水压力等进行分层观测,根据分层观测得到地下水位变化、孔隙水压力变化等数据,并采用回归分析法构建了沉降区域地面沉降与诸影响因子的关联模型,通过对郑徐客运专线开封段进行的地面沉降监测量与同时采用二等精密水准测量得到的监测点沉降量进行对比分析,结果表明本方法的有效性和可靠性。