2007-2017年中国沙尘气溶胶的三维分布特征及输送过程

利用最新发布的CALIPSO产品，构建了2007-2017年中国沙尘气溶胶的三维分布，并结合HYSPLIT-4模式和再分析数据，探讨了沙尘的三维输送过程。结果表明：中国的沙尘排放源区主要是塔克拉玛干沙漠和巴丹吉林沙漠，沙尘气溶胶出现频率分别为60%和35%。塔克拉玛干沙漠排放的沙尘主要（50%~70%）停留在源地0~6 000 m高度，少部分向东输送至甘肃和内蒙古；巴丹吉林沙漠排放的沙尘则主要向东输送。中国沙尘排放量在春季最大，向东输送最强；夏季，东亚夏季风限制了沙尘向东输送；秋季，沙尘排放减弱，输送强度和夏季相当；沙尘排放量在冬季最小，输送最弱。夏季，沙尘在输送过程中可被抬升至高度5 000 m以上，春季次之，秋、冬季的沙尘主要在低层大气输送。沙尘在向东输送的过程中被抬升并和当地人为污染物混合变为污染性沙尘，华北地区污染性沙尘出现频率高达30%；输送到海洋的沙尘也会与洋面上（0~3 000 m高度）的海盐气溶胶混合，出现频率约为10%。     

Temporal-Spatial Variation of Surface Suspended Matter and Controlling Factors in the Inner Shelf of the East China Sea in Winter

Based on the observed surface suspended matter in the East China Sea in February 2007 and June 2015, an empirical model was established using L1 b's band 4 data to retrieve surface suspended matter from the Moderate Resolution Imagine Spectroradiometer Terra imagery. The squared correlation coefficient is 0.8358, and the root mean square error is 0.4285 mg L-1. The model reflects the distribution characteristics of surface suspended matter in the inner shelf of the East China Sea. In this paper, the satellite images of the study area were retrieved in January from 2001 to 2015, and the monthly distribution of surface suspended matter were obtained. The inter-annual distribution of the study area is similar, and the concentration of surface suspended matter is higher near the shore than offshore. A large amount of surface suspended matter is transported southeast under the influence of Zhejiang and Fujian coastal current and Taiwan warm current. Only a small amount of surface suspension can reach the Kuroshio area. The surface suspended matter concentration changes obviously near the estuary because of the effect of differences in the flux of the Yangtze River. Meanwhile, winter monsoon, temperature front, El Ni?o events, and other factors affect the distribution of surface suspended matter in 100 m isobath to coastal water but minimally influence the distribution in 100 m isobath to deep sea.     

Solar GLAO and TAO:performance comparisons

Multi-conjugate adaptive optics(MCAO),consisting of several deformable mirrors(DMs),can significantly increase the adaptive optics(AO)correction field of view.Current MCAO can be realized by either star-oriented or layer-oriented approaches.For solar AO,ground-layer adaptive optics(GLAO)can be viewed as an extreme case of layer-oriented MCAO in which the DM is conjugated to the ground,while solar tomography adaptive optics(TAO)that we proposed recently can be viewed as star-oriented MCAO with only one DM.Solar GLAO and TAO use the same hardware as conventional solar AO,and therefore it will be important to see which method can deliver better performance.In this article,we compare the performance of solar GLAO and TAO by using end-to-end numerical simulation software.Numerical simulations of TAO and GLAO with different numbers of guide stars are conducted.Our results show that TAO and GLAO produce the same performance if the DM is conjugated to the ground,but TAO can only generate better performance when the DM is conjugated to the best height.This result has important application in existing one-DM solar AO systems.     

An Early Cretaceous slab window beneath central Tibet,SW China: Evidence from OIB‐like alkaline gabbros in the Duolong area

The geodynamic mechanism of the late Early Cretaceous magmatic flare‐up in the collisional zone between the Lhasa and Qiangtang terranes in Tibet is controversial because of a scarcity of robust evidence. To address this problem, we report geochronological, geochemical and Hf isotopic data for the newly discovered Gufeng gabbros from the Duolong Cu–Au mineral district of the western Bangong–Nujiang Suture Zone (BNSZ). The gabbro samples, dated at 126.3 ± 1.8 Ma, show geochemical similarities to typical ocean island basalt (OIB) and have positive εHf(t) values of +3.3 to +6.9. The gabbros were generated by decompression melting of deep upwelling asthenosphere. This event is best explained by slab break‐off and the resultant development of a slab window beneath central Tibet.     

基于PSR-WSVM模型的边坡位移预测

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。     

Variations in glacier volume and snow cover and their impact on lake storage in the Paiku Co Basin,in the Central Himalayas

Glaciers and snow cover are important constituents of the surface of the Tibetan Plateau. The responses of these phenomena to global environmental changes are sensitive, rapid and intensive due to the high altitudes and arid cold climate of the Tibetan Plateau. Based on multisource remote sensing data, including Landsat images, MOD10A2 snow product, ICESat, Cryosat-2 altimetry data and long-term ground climate observations, we analysed the dynamic changes of glaciers, snow melting and lake in the Paiku Co basin using extraction methods for glaciers and lake, the degree-day model and the ice and lake volume method. The interaction among the climate, ice-snow and the hydrological elements in Paiku Co is revealed. From 2000 to 2018, the basin tended to be drier, and rainfall decreased at a rate of −3.07 mm/a. The seasonal temperature difference in the basin increased, the maximum temperature increased at a rate of 0.02°C/a and the minimum temperature decreased at a rate of −0.06°C/a, which accelerated the melting from glaciers and snow at rates of 0.55 × 10⁷ m³/a and 0.29 × 10⁷ m³/a, respectively. The rate of contribution to the lake from rainfall, snow and glacier melted water was 55.6, 27.7 and 16.7%, respectively. In the past 18 years, the warmer and drier climate has caused the lake to shrink. The water level of the lake continued to decline at a rate of −0.02 m/a, and the lake water volume decreased by 4.85 × 10⁸ m³ at a rate of −0.27 × 10⁸ m³/a from 2000 to 2018. This evaluation is important for understanding how the snow and ice melting in the central Himalayas affect the regional water cycle.     

Retrieving volume FeO and TiO_2 abundances of lunar regolith with CE-2 CELMS data using BPNN method

The volume FeO and TiO_2 abundances(FTAs) of lunar regolith can be more important for understanding the geological evolution of the Moon compared to the optical and gamma-ray results. In this paper, the volume FTAs are retrieved with microwave sounder(CELMS) data from the Chang'E-2 satellite using the back propagation neural network(BPNN) method. Firstly, a three-layered BPNN network with five-dimensional input is constructed by taking nonlinearity into account. Then, the brightness temperature(TB) and surface slope are set as the inputs and the volume FTAs are set as the outputs of the BPNN network.Thereafter, the BPNN network is trained with the corresponding parameters collected from Apollo, Luna,and Surveyor missions. Finally, the volume FTAs are retrieved with the trained BPNN network using the four-channel TBderived from the CELMS data and the surface slope estimated from Lunar Orbiter Laser Altimeter(LOLA) data. The rationality of the retrieved FTAs is verified by comparing with the Clementine UV-VIS results and Lunar Prospector(LP) GRS results. The retrieved volume FTAs enable us to re-evaluate the geological features of the lunar surface. Several important results are as follows. Firstly, very-low-Ti(1.5 wt.%) basalts are the most spatially abundant, and the surfaces with TiO_2 5 wt.% constitute less than 10% of the maria. Also, two linear relationships occur between the FeO abundance(FA) and the TiO_2 abundance before and after the threshold, 16 wt.% for FA. Secondly, a new perspective on mare volcanism is derived with the volume FTAs in several important mare basins, although this conclusion should be verified with more sources of data. Thirdly, FTAs in the lunar regolith change with depth to the uppermost surface,and the change is complex over the lunar surface. Finally, the distribution of volume FTAs hints that the highlands crust is probably homogeneous, at least in terms of the microwave thermophysical parameters.     

毛乌素沙区1961-2016年降水特征

基于毛乌素沙区10个气象站1961-2016年观测资料，应用Mann-Kendall方法和t检验法对各气象站年降水量进行了突变检验，借助小波分析讨论了各气象站年降水量的周期特征，根据降水量等值线划分结果对整个研究区分区分析了年、季、月和日尺度上的降水变化特征，并在两个时段上分析了季节性降水的差异。结果表明：毛乌素沙区年降水量空间特征差异明显，东部亚区呈上升趋势，中西部亚区呈下降趋势，但变化趋势不显著且无突变发生；降水年内分配不均，干湿季分明，降水集中在5-9月，夏秋季降水占全年降水比重大，季、月和日尺度降水量存在梯度递减变化；年降水量的年际变化过程存在多重时间尺度的自相似结构；近26年的冬春季降水增加显著，但降水波动幅度小于前30年。     

Classification of stellar spectra with SVM based on within-class scatter and between-class scatter

Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies.     

经济地理学视角下创新网络与区域增长研究述评

创新是引领经济发展的第一动力，创新与区域增长之间的关系成为经济地理学者关注的重点领域。文章通过梳理近年来有关创新网络测度、创新网络与区域增长关系、创新网络作用于区域增长方式等方面的文献发现：网络资本可以弥补社会资本在解释企业创新结网经济价值方面的不足，区域增长呈现出网络化特征已成为学界共识，知识流与邻近性能较好地解释创新网络与区域增长的关系机理。然而，当前研究对社会资本如何促进区域增长，网络资本与区域增长关系模型如何构建，不同类型的邻近性与知识对区域增长的影响有何差异等问题尚不明确，建议重视网络资本对区域增长的作用并实证检验二者的关联，同时还应重视创新网络中企业家精神、创新个体心理行为特征等因素对区域增长的影响。