寡照对温室黄瓜花果期生长及产量品质影响研究

为了探讨寡照对日光温室黄瓜花果期生长发育、产量和品质的影响,以黄瓜品种“德瑞特L108”(Cucumis sativusL. cv.Derit L108)为试材,通过不同遮阴日数（1 d、3 d、5 d、7 d、9 d）及恢复控制试验,研究寡照对日光温室黄瓜生长及果实品质的影响。结果表明：1）持续寡照日数超过5 d,黄瓜株高和茎粗生长受到显著影响,尤其是茎粗存在变细现象,7 d以上对植株生长影响不可逆。2）黄瓜果实横径、长度生长速率和果重受寡照影响程度存在一定差异,果实上下两端横径长势对寡照较为敏感,连续寡照3 d时影响即达显著水平,可导致上端横径生长速率降低0.87 mm·d-1,下端为0.73～0.99 mm·d-1;果实中部横径和长度增长速率在寡照5 d以上时影响达显著水平,生长速率降低0.88 mm·d-1和0.87～1.00 cm·d-1;与对照（以CK表示）相比,寡照3 d的单果重降低18.7 g,寡照日数每延长1 d,单果重降低2～3 g。3）果实的外观等级受寡照影响较大,1～5 d寡照,特级果比例降低9.4%～43.0%;持续寡照5 d以上二级果和坏果比例明显升高,连续寡照9 d,二级果和坏果比例达到20%和25%;寡照3 d以上,果实含水率与CK差异即达到显著性差异;维生素含量对寡照影响反映敏感,1～5 d寡照,果实维生素含量降低6%左右,7 d以上的寡照日数,则降低10%～13%;寡照持续日数超过5 d,花青素和有机酸含量影响较大;持续寡照日低于7 d时,可溶性糖含量对寡照影响敏感度相对较低,超过7 d其含量达到显著性差异,且寡照日每延长1 d,其含量降低4%。     

纬向切变基流中的非线性Rossby波

本文在半地转近似下讨论了纬向切变基流中的非线性正压Ｒｏｓｓｂｙ波，给出了存在波解的切变基流条件。     

Ekman动量近似下中间边界层模式中的风场结构

发展了一个准三维的、中等复杂的边界层动力学模式，该模式包含了EKman动量近似下的惯性加速度和Blackadar的非线性湍流粘性系数，它进一步改进了Tan和Wu(1993）提出的边界层理论模型。该模型在数值计算复杂性上与经典Ekman模式相类似，但由于包含了Ekman动量近似下的惯性项，使得该模式比传统Ekman模式更近于实际过程。中详细地比较了该模式与其他简化边界层模式在动力学上的差异，结果表明：在经典的Ekman模式中，由于忽略了流动的惯性项作用，导致在气旋性切变气流（反气旋性切变气流）中风速和边界层顶部的垂直速度的高估（低估），而在半地转边界层模式中，由于高估了流动惯性项的作用，结果与经典Ekman模式相反。同样，该模式可以应用于斜压边界层，对于Ekman动量下的斜压边界层风场同时具有经典斜压边界层和Ekman动量近似边界层的特征。     

基于压缩感知的地基红外云图云状识别

为了对地基全天空红外测云仪获得的云图进行分类,该文从压缩感知理论出发,提出了一种利用云图灰度稀疏性进行云状识别的新方法。首先运用典型云图样本构造冗余字典,然后通过梯度投影 (GPSR) 算法和正交匹配 (OMP) 算法求取测试样本在冗余字典中的l1范式最优解,最后利用残差法和稀疏比例法对云状进行判别并输出。采用压缩感知理论进行云状识别,降低了对特征提取技术的要求,为云状的自动识别提供了新思路,对典型波状云、层状云、积状云、卷云和晴空的总体识别率分别达到75%,91%,70%,85%和93%,平均识别率为82.8%。     

我国月降水和气温网格点资料的处理和分析

用逐步搜索逼近法的客观分析技术计算出１９５１－１９９２年中国月降水量和月平均气温的格点资料，并利用图象识别原理对格点值和站点观测值进行了比较。     

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.     

高光谱影像概率分类向量机分类方法研究

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。     

基于人工鱼群算法和SCA等效理论的井中横波速度预测方法

在建立页岩岩石物理模型的基础上,根据等效自相容近似(SCA)岩石物理模型,构建出岩石的纵波速度、横波速度与岩石密度、组分和孔隙度等的定量关系,得出使理论纵波速度和实际纵波速度最接近的孔隙纵横比,进而将该孔隙纵横比作为约束条件来实现横波速度预测。反演算法利用人工鱼群算法来计算最佳孔隙纵横比,并将预测的横波速度与实际测得的横波速度对比,证明了人工鱼群算法的有效性。     

基于改进K-SVD字典学习方法的地震数据去噪

为实现更好的地震数据去噪技术,笔者引入一种新的算法:快速迭代收缩阀值法(FISTA),通过FISTA和K-奇异值分解(K-SVD)不断迭代更新K-SVD字典,利用更新得到的K-SVD字典对地震数据进行稀疏表示,去除稀疏系数中较小的数值,使数据中的随机噪声得到压制。对层状模型合成地震记录,Marmousi模型合成地震记录以及实际地震数据进行对比实验,得出FISTA算法较OMP算法能更好地提高地震数据的信噪比,同时有效地保护了反射信号。     

Generation of oceanic internal gravity waves by a cyclonic surface stress disturbance

Atmospheric cyclones with strong winds significantly impact ocean circulation, regional sea surface temperature, and deep water formation across the global oceans. Thus they are expected to play a key role in a variety of energy transport mechanisms. Even though wind-generated internal gravity waves are thought to contribute significantly to the energy balance of the deep ocean, their excitation mechanisms are only partly understood.The present study investigates the generation of internal gravity waves during a geostrophic adjustment process in a Boussinesq model with axisymmetric geometry. The atmospheric disturbance is set by an idealized pulse of cyclonic wind stress with a Rankine vortex structure. Strength, radius and duration of the forcing are varied. The effect upon wave generation of stratification with variable mixed-layer depth is also examined.Results indicate that internal gravity waves are generated after approximately one inertial period. The outward radial energy flux is dominated by waves having structure close to vertical mode-1 and with frequency close to the inertial frequency. Less energetic higher mode waves are observed to be generated close to the sea floor underneath the storm. The total radiated energy corresponds to approximately 0.02% of the wind input. Deeper mixed-layer conditions as well as weaker stratification reduce this fraction.The low energy transfer rates suggest that other processes that drive vertical motion like surface heat fluxes, turbulent motion, mixed region collapse and storm translation are essential for significant energy extraction by internal gravity waves to occur.