TRMM星载测雨雷达和地基雷达反射率因子数据的三维融合

TRMM卫星上的测雨雷达(TRMMPR)探测资料分布均匀且具有很高的垂直分辨率,但灵敏度较低;地基雷达(GR)水平分辨率较高且具有较高的灵敏度,但其垂直分辨率低。通过将TRMM PR与GR反射率因子数据的三维数据融合,得到了更优的反射率因子图像。测雨雷达与地基雷达三维数据融合主要分为以下几步:测雨雷达与地基雷达数据预处理——如去杂波、衰减校正;测雨雷达与地基雷达时空匹配;选取和应用合适的三维图像融合算法;对融合后的图像进行效果评估。试验结果表明:融合后的图像不仅增大了信息量,更好地检测弱降水,还提高了空间三维(3D)分辨率,能更好地反映降水区域细节,且使得数据总体上具有更高的完整性和可靠性。此外,还将基于雷达资料估测的降水数据与地面雨量计数据进行对比,估计反射率因子数据融合在降水测量上的有效性。      

Spatially explicit assessment of water embodied in European trade: A product-level multi-regional input-output analysis

Responsible water management in an era of globalised supply chains needs to consider both local and regional water balances and international trade. In this paper, we assess the water footprints of total final demand in the EU-27 at a very detailed product level and spatial scale—an important step towards informed water policy. We apply the multi-regional input-output (MRIO) model EXIOBASE, including water data, to track the distribution of water use along product supply chains within and across countries. This enables the first spatially-explicit MRIO analysis of water embodied in Europe’s external trade for almost 11,000 watersheds world-wide, tracing indirect (“virtual”) water consumption in one country back to those watersheds where the water was actually extracted. We show that the EU-27 indirectly imports large quantities of blue and green water via international trade of products, most notably processed crop products, and these imports far exceed the water used from domestic sources. The Indus, Danube and Mississippi watersheds are the largest individual contributors to the EU-27’s final water consumption, which causes large environmental impacts due to water scarcity in both the Indus and Mississippi watersheds. We conclude by sketching out policy options to ensure that sustainable water management within and outside European borders is not compromised by European consumption.     

中国中东部冬季霾日的形成与东亚冬季风和大气湿度的关系

利用1961-2013年中国地面台站长期观测资料和同期NCEP/NCAR再分析资料,以华北、江淮和华南为研究区,分析了中国中东部冬季霾日的形成与东亚冬季风以及大气湿度的关系。结果表明:(1)冬季霾日与东亚冬季风强度成显著的负相关。首先,东亚冬季风强度的减弱使得地面风速减小,进而导致冬季霾日增多。其中,华北7-8 m/s最大风速日数和江淮6-8 m/s最大风速日数的减少,及华南≤2 m/s最大风速日数的增多对各区冬季霾日的增多作用较大。其次,东亚冬季风减弱引起冬季气温的持续升高,易导致冬季霾日的增多,这在华北地区较之在江淮和华南更为明显。(2)由于气候变暖,冬季气温升高,使得近地面相对湿度减小。在江淮和华南地区,冬季霾日的增多与近地面相对湿度的减小显著相关,而在华北地区这种相关较弱。(3)冬季气温升高也有利于大气层结稳定度的增强,3个区域冬季霾日的增多均与大气层结稳定度的增强显著相关,特别是与对流层中低层(850-500 hPa)大气饱和度的降低显著相关。(4)冬季霾日数变化与区域水汽输送关系密切。其中,华北地区的冬季霾日数与水汽总收入成显著正相关,江淮地区与纬向水汽收入成显著正相关,与经向水汽收入成显著负相关,华南地区与经向水汽收入成显著负相关。     

江苏一次锢囚状MCS和相关中涡旋MCV的观测分析

利用常规地面和高空气象观测资料,结合气象卫星云图和雷达回波,分析了2009年6月14日15—23时(北京时,下同),造成江苏强对流天气的一个中尺度对流系统(MCS)的锢囚状特征的形成过程及其垂直结构。地面中尺度分析表明,雷暴高压东侧在飑前倒槽北端发展的闭合低压环流的东南气流将暖湿空气输送到冷性雷暴高压的北侧形成东南一西北向的暖舌,从而形成锢囚状的结构。长三角探空网资料的垂直结构分析表明,在对流层下部地面到850 hPa为冷性的雷暴高压,在对流层中部700 hPa为冷性的α中尺度涡旋(MCV),而500 hPa已转变为暖性的MCV。静力学关系可以说明MCV仅仅存在于700~500 hPa的原因和MCS下冷上暖的热力结构密切相关。     

Logistic判别模型在强降水预报中的应用

利用Logistiv判别模型进行强降水预报,并设计3种方案进行对比分析。方案1直接使用14个影响因子进行判别预报,受因子共线性作用及噪音信号影响,虽然拟合效果较好,但预报效果明显下降。方案2对14个影响因子进行主成分分析,利用前6个主成分建模,虽然拟合效果较方案1降低,但由于消除了因子共线性作用以及噪音信号影响,预报效果较方案1提高。方案3运用Bootstrap抽样技术得到符干样本并建模计算模型参数,打乱了原有时间序列中的波动,仪保留平稳信息,拟合自由度进一步降低,导致拟合效果较方案案2下降,但预报效果却是3种方案中最好且最稳定的。在上述研究基础上,利用欧洲中心数值预报模式的预报场资料,建立基于Logistic判别模型的强降水客观预报系统,并在中央气象台业务运行。2013和2014年连续两年汛期预报检验结果表明,概模型对强降水预报的TS评分高于数值模式本身,具有一定的业务参考价值。     

风暴分类识别技术在人工防雹中的应用

利用新一代多普勒天气雷达资料,在风暴跟踪识别算法的基础上,发展了风暴分类技术,以提高人工防雹作业指挥的效率。首先以SCIT算法为基础,结合风暴的结构特征,综合利用雷达、探空资料,自动提取风暴结构特征指数;其次采用基于决策树模型的风暴自动分类技术,将风暴按强度分为雷雨云、单体风暴、多单体风暴和强风暴;最后根据风暴强度、高度和位置等属性,对有可能产生冰雹的单体,结合GIS,自动对下游方向或附近作业点进行预警或输出作业参数。通过对2006—2014年期间重庆、辽宁大连和河南三门峡三地发生的较为典型的31次冰雹天气过程、182站次冰雹样本的检验来看:该方法通过对风暴按强度、垂直结构等综合属性进行分类,能有效提高冰雹识别的命中率、降低空报率,其中强风暴的命中率能达到100%,空报率仅为11.4%。能有效提高人工防雹作业的自动化程度,对防雹作业的科学决策有着重要参考作用。     

CINRAD/SA(B)发射机高压打火组件级故障诊断

新一代天气雷达技术保障中,发射机高压负载打火导致的综合故障的诊断和定位是比较复杂的,具有故障点多、故障涉及组件多、故障修复时间长等特点,是新一代天气雷达故障维修的难点。依据发射机高压控制和监控信号流程,提出了发射机高压打火组件级故障诊断流程。通过发射机组件级故障诊断流程快速修复发射机高压负载打火综合故障过程,表明发射机高压打火组件级故障诊断流程在雷达维修中具有规范化和适用性维修效果,进一步显示出故障分析诊断流程在新一代天气雷达技术保障中的重要作用。     

Petrogenesis and geodynamic evolution of the Kajan Neogene subvolcanic rocks,Nain, Central Iran

Kajan subvolcanic rocks in the Urumieh–Dokhtar magmatic arc (UDMA), Central Iran, form a Late Miocene-Pliocene shallow-level intrusion. These subvolcanics correspond to a variety of intermediate and felsic rocks, comprising quartz diorite, quartz monzodiorite, tonalite and granite. These lithologies are medium-K calc-alkaline, with SiO₂ (wt.%) varying from 52% (wt.%) to 75 (wt.%). The major element chemical data also show that MgO, CaO, TiO₂, P₂O₅, MnO, Al₂O₃ and Fe₂O₃ define linear trends with negative slopes against SiO₂, whilst Na₂O and K₂O are positively correlated with silica. Contents of incompatible trace elements (e.g. Ba, Rb, Nb, La and Zr) become higher with increasing SiO₂, whereas Sr shows an opposite behaviour. Chondrite-normalized multi-element patterns show enrichment in LILE relative to HFSE and troughs in Nb, P and Ti. These observations are typical of subduction related magmas that formed in an active continental margin. The Kajan rocks show a strong affinity with calc-alkaline arc magmas, confirmed by REE fractionation (La_N/Yb_N = 4.5–6.4) with moderate HREE fractionation (Sm_N/Yb_N = 1.08–1.57). The negative Eu anomaly (Eu/Eu* <1), the low to moderate Sr content (< 400 ppm) and the Dy/Yb values reflect plagioclase and hornblende (+- clinopyroxene) fractionation from a calc-alkaline melt Whole–rock Sr and Nd isotope analyses show that the ⁸⁷Sr/⁸⁶Sr initial ratios vary from 0.704432 to 0.705989, and the ¹⁴³Nd/¹⁴⁴Nd initial ratios go from 0.512722 to 0.512813. All the studied samples have similar Sr-Nd isotopes, indicating an origin from a similar source, with granite samples that has more radiogenic Sr and low radiogenic Nd isotopes, suggesting a minor interaction with upper crust during magma ascent. The Kajan subvolcanic rocks plot within the depleted mantle quadrant of the conventional Sr-Nd isotope diagram, a compositional region corresponding to mantle-derived igneous rocks.     

Landscape pattern and transition under natural and anthropogenic disturbance in an arid region of northwestern China

There is a pressing need to determine the relationships between driving variables and landscape transformations. Human activities shape landscapes and turn them into complex assemblages of highly diverse structures. Other factors, including climate and topography, also play significant roles in landscape transitions, and identifying the interactions among the variables is critical to environmental management. This study analyzed the configurations and spatial-temporal processes of landscape changes from 1998 to 2011 under different anthropogenic disturbances, identified the main variables that determine the landscape patterns and transitions, and quantified the relationships between pairs of driver sets. Landsat images of Baicheng and Tekes from 1998, 2006 and 2011 were used to classify landscapes by supervised classification. Redundancy analysis (RDA) and variation partitioning were performed to identify the main driving forces and to quantify the unique, shared, and total explained variation of the sets of variables. The results indicate that the proportions of otherwise identical landscapes in Baicheng and Tekes were very different. The area of the grassland in Tekes was much larger than that of the cropland; however, the differences between the grassland and cropland in Baicheng were not as pronounced. Much of the grassland in Tekes was located in an area that was near residents, whereas most of the grassland in Baicheng was far from residents. The slope, elevation, annual precipitation, annual temperature, and distance to the nearest resident were strong driving forces influencing the patterns and transitions of the landscapes. The results of the variation partitioning indicated complex interrelationships among all of the pairs of driver sets. All of the variable sets had significant explanatory roles, most of which had both unique and shared variations with the others. The results of this study can assist policy makers and planners in implementing sustainable landscape management and effective protection strategies.     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.