内蒙古河套灌区春玉米作物系数试验研究

作物系数曲线是估算作物生长季耗水量变化的重要参数。基于2013年4—9月内蒙古巴彦淖尔市临河区田间水分试验和1994—2013年气象站观测资料,利用水量平衡法反求春玉米作物系数,分析生长季内的变化规律, 建立动态模拟方程,并与联合国粮农组织 (FAO) 分段直线法结果进行比较, 提出胁迫条件下作物系数的叶面积修正方法。结果表明:玉米作物系数随发育进程可用三项式曲线描述,变化趋势与产量水平无关, 但随产量增高而变幅增大;以出苗后相对积温为时间变量建立模拟方程效果较好,决定系数 (R2) 均在0.92以上;模拟计算出各站点最大 (1.30~1.48) 和平均 (0.831~0.919) 作物系数,与FAO分段直线法计算的典型值和区间值基本一致,生长中期平均相对误差为3.4%~7.2%;提出利用相对叶面积指数修正作物系数的计算方法;通过2014年实例检验,土壤水分模拟值与实测值的平均相对误差为6.3%,相对误差小于15%的占95.8%。     

华南暖区暴雨研究进展

华南前汛期暴雨预报一直是我国大气科学界的一个研究热点,特别是发生在锋前的暖区暴雨,由于其天气尺度斜压性强迫不明显,环境大气水汽含量丰富,热动力不稳定性强,边界层触发机制复杂,以及特殊的地形和海陆热力差异的外强迫作用,导致暴雨突发性强,地域性特征显著,也是困扰预报业务人员的难点问题。目前我国预报业务中使用的全球数值预报模式对暖区暴雨的预报能力十分有限,高分辨率中尺度数值模式的预报效果也不尽人意。该文回顾了近40年华南前汛期暴雨大部分研究成果,针对华南暖区暴雨的提出及典型背景场、暖区暴雨与低空急流的关系、暖区中尺度对流系统的形成及传播、暖区暴雨触发机制等独特的天气动力学特征进行了系统梳理与分析,并依据前人研究成果及中央气象台预报实践经验,总结提炼了3类华南暖区暴雨类型——边界层辐合线型、偏南风风速辐合型,以及强西南急流型的天气系统配置及触发因子。最后提出针对华南暖区暴雨需要进一步研究的科学问题。     

气象、农业和水文干旱之间关联性分析

干旱具有发生频率高、持续时间长、波及范围广的特点。而干旱预报为科学地进行防旱抢险提供了决策支持。选取反映不同类型干旱的指标,即标准化降雨指标(SPI)、标准化土壤湿度指标(SSWI)和标准化径流指标(SRI),通过SWAT模型和带有时滞的灰色关联判断了各干旱之间的时滞。以陆浑水库控制流域为例进行了分析,结果表明:SWAT模型在该流域有很好的适用性,1975—2009年间发生各类干旱的次数在增加,且变率上从气象干旱、农业干旱到水文干旱有所增加,同时不同类型干旱之间表现出了一定的时滞关系,气象干旱对农业干旱的响应时间为1个月;水文干旱对气象干旱的响应时间为4个月;水文干旱对农业干旱的响应时间为2个月。     

新疆西南部一次局地对流性暴雨成因分析

利用常规地面和探空气象资料、NCEP逐6h1°×1°的再分析资料和(CINRAD-CC)多普勒雷达探测资料,对2012年7月19日新疆阿图什罕见的短时对流性暴雨形成原因进行了诊断分析。结果表明:此次天气是在南疆低涡有利的环流背景下、低层中尺度低涡促进上升运动及触发不稳定能量释放产生的;冷空气的入侵是造成对流扰动发展的重要原因,同时地面小尺度系统对触发不稳定能量释放也有一定的作用;涡散场的配置、垂直速度、低层偏东急流和强不稳定能量满足了对流发生的基本条件,而较强的垂直风切变则使风暴明显增强;从喀什探空订正图来看,修正后的探空资料中各要素对开展强对流潜势预报有很好的指示意义,有待进一步总结验证;对多普勒雷达资料的分析表明,此次强降水具有强回波、强的垂直风切变、大的垂直液态水含量和较高回波顶高等,与强对流天气的发生发展及落区有较好的对应关系。     

风云卫星数据服务评价指标的验证分析

简述了目前我国风云卫星数据服务以及气象服务效益评估业务的现状,利用层次分析法提出风云卫星数据服务评价的6个1级指标和19个2级指标。设计了风云卫星数据服务评测量表,并采用分半信度、因子分析等方法对量表作信度和效度分析,结果表明所设计的评测量表具有良好的信度和效度,确定了包括服务平台易用性、服务流程规范性、服务响应及时性、服务内容完整性、服务工作主动性、服务宣传广泛性等6个方面的风云卫星数据服务评价指标。     

海风雷暴的观测分析和数值模拟研究进展

沿海地区经济相对繁荣,城市化水平较高,对天气和气候的依赖性强,突发性强对流天气所造成的灾害也会更加严重;同时沿海地区的强对流天气又与海风环流密切相关,因此沿海地区海风雷暴的研究受到了日益广泛的关注,成为了气象学和大气科学中的重要研究对象。在过去的半个多世纪中,海风雷暴的观测和模拟研究取得了大量的研究成果。本文通过对这些研究工作进行回顾和总结,系统地分析了国内外的研究现状,重点讨论了海风雷暴的结构和特征、发展演变过程、触发机制及其预报预警。最后对海风雷暴未来的研究方向进行了探讨,提出了一些有待于研究或需深入研究的问题,以利于今后更好的开展有关海风雷暴的工作,加深对其发生发展规律的认识,提高预报预警水平。     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

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.     

M-Quadtree索引:一种基于改进四叉树编码方法的云存储环境下空间索引方法

为了解决基于"键-值"模型的云存储环境仅支持简单的关键字查询,不支持多维空间查询的问题,提出了一种新的分布式空间索引方法——M-Quadtree索引。在索引构建过程中,设计了一种基于改进四叉树的空间数据划分方法,该方法规定了叶节点区域的最小数据量,通过四叉树叶节点的再合并,解决了划分后各子区域间存储量不平衡的问题,并且满足了MapReduce并行化要求。给出了MapReduce框架下M-Quadtree索引的快速构建、查询与更新算法,并在搭建的Hadoop平台进行了关键参数对索引效率的影响以及不同规模数据下索引的创建、查询和更新试验。与现有分布式空间索引的对比试验及分析结果表明,M-Quadtree索引在数据存储量负载均衡、算法并行化和空间查询效率等方面表现得更好。     

时空信息云平台下数据管理及可视化方法

针对智慧城市时空信息云平台中海量时空数据的管理和可视化问题,该文提出了一种时空索引建立及时空数据可视化方法。时空索引机制将时间、空间与专题属性作为同等重要的维度,兼顾了三者的查询效率,实现对时空数据的快速有效提取。时空数据可视化表达方法提出了时间轴动画、多时态对比、对象状态回溯等可视化方式,实现时空数据的动态交互式表达。该方法在"智慧常州时空信息云平台"项目中进行了实现,证明这种时空数据组织管理方法可较高效地实现数据的检索调度,且具有较强的可操作性;基于此的时空数据可视化方法亦具备较佳的时空对象动态表达效果。