Contact Binaries: A Study of the Proximity Effects and Gravity Darkening Based on Kopal’s Fourier Method

A new method for the determination of the proximity effects and gravity darkening exponents in contact binaries of W UMa type is presented. The method is based on Kopal’s method of Fourier analysis of the light changes of eclipsing variables in the Frequency Domain. The method was applied to 36 W UMa systems for which geometric and photometric elements have been derived by the most powerful techniques. The derived values are very close to those predicted by the existing theory of radiative transfer or convective equilibrium.     

UV Leo: The Binary with the Two Suns

BV light curves of the eclipsing binary UV Leo obtained at the Kryonerion Astronomical Station of the National Observatory of Athens, Greece, are analyzed. The analysis is based on a Roche configuration with two spots on the secondary surface. The elements of the two components of the system are calculated and the spot characteristics are given.     

激变食变星PG0027+260的CCD光变曲线

我们于1989年11月30日晚对PG0027+260进行了时间分辨率为108秒的高速CCD测光,得到了一条完整的光变曲线,从而确认其为激变食变星系统,轨道周期0.146~d     

On the Period Variation of the W UMa-type Contact Binary V502 Ophiuchi

The variation in the orbital period of the W UMa type contact binary V502 Oph is analyzed. The orbital period exhibits a wavelike variation with a periodicity of 23.0 years and an amplitude of △P = 1.24×10~(-6) days superimposed on secular decrease of dP/dt = 1.68×10-7 day per year. The long-term decrease may be accompanied by the contraction of the secondary at a rate of 83 m per year and a mass transfer rate from the primary to the secondary of 4.28×10~8 M per year. The short-term oscillation may be explained by the presence of a third component. Orbital elements of the third body and its possible mass are presented.     

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.     

高维遥感图像的快速分类算法

为了实现对高维遥感图像的快速准确分类,提出了一种基于k均值二叉树支持向量机(SVM)的分类方法。该方法通过对选取的训练样本进行k均值聚类,生成支持向量机分类二叉树,作为确定最佳分类顺序的依据,以降低分类过程中的误差累积并提高整体分类精度,而且可缓解由样本数量不均衡导致的分类误差。该方法可在不进行降维处理的情况下,对高维遥感图像进行快速准确分类。测试结果表明,其分类速度和分类精度都优于传统的支持向量机分类结果。     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models: An Empirical Validation of ISWRidge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.     

HS Herculis: Not apsidal motion,but a third body

Todoran's (1992) postulate of apsidal motion to explain the observed minimum times of HS Her is shown to be physically inconsistent. Instead, a third body in the system is postulated. Minimum timings in the next few years can settle the matter.     

台风菲特暴雨诊断分析

"菲特"台风暴雨具有阶段性特征,包括台风远距离降水、台风倒槽和内螺旋雨带降水、台风外部螺旋雨带降水、台风残留低压环流与冷空气相互作用降水4个阶段。利用地面观测、气象雷达观测、NCEP分析资料,对"菲特"台风暴雨环流形势进行了分析。引导台风东移的高压东西部具有不同热力属性,东部暖性深厚、西部冷性浅薄。浅薄冷高压阻挡登陆台风继续西移,延长台风倒槽和外围螺旋雨带的降水时间。"丹娜丝"台风的靠近,有利于东南风水汽输送的增强。副热带高压的增强,在黄海上空逼近东移高空槽形成稳定的高空急流。文章提出与传统垂直风切变大、高空急流强的冷空气阻挡型不同的侵入型冷空气和台风相互作用形势。侵入型冷空气从低层入侵,影响台风残留低压的外围环流。在低压外围环流的北部形成较强的东北风,并与海上的东风对峙辐合形成海岸锋。冷空气侵入型的空间不对称特征明显:对流有效位能东高西低,垂直风切变西北高东南低。残留低压的中层受冷空气影响较小,沿海地区的东南风持续的时间更长。中层高位涡区与地面海岸锋的互应,为变性的台风残留低压暴雨提供有利条件。