水稻叶面积指数的高光谱遥感估算模型

通过不同氮素营养水平的水稻田间试验 ,采用单变量线性与非线性拟合模型和逐步回归分析 ,用1 999年试验数据为训练样本 ,建立水稻LAI的高光谱遥感估算模型 ,用 2 0 0 0年试验数据作为测试样本数据 ,对其精度进行评价和验证。结果表明 ,高光谱变量与LAI之间的拟合分析中 ,蓝边内一阶微分的总和与红边内一阶微分的总和的比值和归一化差植被指数是最佳的变量     

丹江口水库叶绿素a浓度的时空特征及影响因子分析

2017年5月—2019年10月,对"南水北调"中线水源水库——丹江口水库库区水体7个监测位点、0～20 m间4个水层的垂向叶绿素a (Chl.a)浓度与水质因子进行了季度性调查,以期了解不同位点、不同水层Chl.a分布的主要驱动因子.结果表明,丹江口水库各位点营养状态指数(TSI)均为中营养化状态.水体Chl.a浓度具有逐年增加的趋势,且极高值有逐渐增加的趋势.营养盐和Chl.a浓度均存在较大的空间异质性,入库区具有较高的总磷和氨氮浓度,汉江库区具有最高的Chl.a浓度,源头污染源控制和监测仍然是丹江口水库管理的重中之重.不同位点Chl.a浓度的驱动因子存在较大差异,汉江入库和大坝区Chl.a浓度分别受到硝态氮和p H的影响,而出水口大坝位点主要受到了水深、水温和氨氮的影响.丹江入库区Chl.a浓度受到了水深、氨氮、总磷和水温的影响,但丹江库区表现出了与其他生态区较大的区别,其Chl.a浓度主要受到水深和有机质输入的影响.因此,对丹江口水库各位点的管理,应该分不同生态区采取针对性的管理措施.本研究旨在为南水北调中线工程可持续的生态调度提供基础生态数据支持,为完善水库水源地的有效管理提供理论支撑.     

A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988–2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern‐match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain ～88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll‐a. We obtained five clusters for the period 1994–1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters.     

塔里木盆地下志留统塔塔埃尔塔格组沉积体系及沉积模式

通过对塔里木盆地众多野外剖面详细观测及钻井岩心的详细观察,研究区下志留统塔塔埃尔塔格组主要沉积一套以砂、泥岩为主的碎屑岩,根据岩性特征、生物标志和地球物理特征,将塔里木盆地塔塔埃尔塔格组划分为滨岸和陆棚沉积体系,滨岸沉积体系进一步划分为无障壁海岸和有障壁潮坪沉积,陆棚沉积体系以浅水陆棚沉积为特征,并就各微相的岩石类型、粒度分布和沉积构造特征进行了详细的研究。在此基础上,分析了塔塔埃尔塔格沉积期的平面展布特征,总体格局为南北向分带,东西向展布为特征。同时建立了塔里木盆地塔塔埃尔塔格组滨岸、潮坪和滨岸－潮坪的沉积模式,在研究区以滨岸－潮坪的沉积模式为主。     

利用方差分量估计的地震同震滑动分布反演

提出了基于方差分量估计的地震同震滑动分布反演方法,该方法不仅可以确定不同数据集的相对权重,而且还能得到平滑因子的值,突破了方差分量估计方法仅用于各类数据定权的实际应用范围。模拟算例验证了此方法的有效性,从解的概率后验密度分布分析来看,方差分量估计方法得到的结果是线性和非线性混合方法反演结果的一个解。用方差分量估计方法反演了Bam地震同震滑动分布,并与线性非线性混合方法反演的结果进行了对比分析。两种方法得到的Bam地震断层滑动分布之间的空间互相关系数为0.999 9,地震同震的最大滑动量相同,都为3.04 m;其深度仅有微小区别,前者为4.50 km,后者为4.47 km;平均滑动量前者为0.714 m,后者为0.718 m。但前者反演计算用时227 s,后者却达到1.5×10⁶ s,约17 d,前者仅为后者的1/6 608。结果表明,本文方法相对于线性和非线性混合方法具有计算简单、计算量小和计算效率高的优点。     

A Note on the Flux-Variance Similarity Relationships for Heat and Water Vapour in the Unstable Atmospheric Surface Layer

Atmospheric surface layer (ASL) experiments over the past 10 years demonstrate that the flux-variance similarity functions for water vapour are consistently larger in magnitude than their temperature counterpart. In addition, latent heat flux calculations using the flux-variance method do not compare as favorably to eddy- correlation measurements when compared to their sensible heat counterpart. These two findings, in concert with measured heat to water vapour transport efficiencies in excess of unity, are commonly used as evidence of dissimilarity between heat and water vapour transport in the unstable atmospheric surface layer. In this note, it is demonstrated that even if near equality in flux-profile similarity functions for heat and water vapour is satisfied, the flux-variance similarity functions for water vapour are larger in magnitude than temperature for a planar, homogeneous, unstably-stratified turbulent boundary-layer flow.     

Source identification of PM2.5 particles measured in Gwangju, Korea

The UNMIX and Chemical Mass Balance (CMB) receptor models were used to investigate sources of PM_2.5 aerosols measured between March 2001 and February 2002 in Gwangju, Korea. Measurements of PM_2.5 particles were used for the analysis of carbonaceous species (organic (OC) and elemental carbon (EC)) using the thermal manganese dioxide oxidation (TMO) method, the investigation of seven ionic species using ion chromatography (IC), and the analysis of twenty-four metal species using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS). According to annual average PM_2.5 source apportionment results obtained from CMB calculations, diesel vehicle exhaust was the major contributor, accounting for 33.4% of the measured PM_2.5 mass (21.5 μg m^− 3), followed by secondary sulfate (14.6%), meat cooking (11.7%), secondary organic carbon (8.9%), secondary nitrate (7.6%), urban dust (5.5%), Asian dust (4.4%), biomass burning (2.8%), sea salt (2.7%), residual oil combustion (2.6%), gasoline vehicle exhaust (1.9%), automobile lead (0.5%), and components of unknown sources (3.4%). Seven PM_2.5 sources including diesel vehicles (29.6%), secondary sulfate (17.4%), biomass burning (14.7%), secondary nitrate (12.6%), gasoline vehicles (12.4%), secondary organic carbon (5.8%) and Asian dust (1.9%) were identified from the UNMIX analysis. The annual average source apportionment results from the two models are compared and the reasons for differences are qualitatively discussed for better understanding of PM_2.5 sources.Additionally, the impact of air mass pathways on the PM_2.5 mass was evaluated using air mass trajectories calculated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model. Source contributions to PM_2.5 collected during the four air mass patterns and two event periods were calculated with the CMB model and analyzed. Results of source apportionment revealed that the contribution of diesel traffic exhaust (47.0%) in stagnant conditions (S) was much higher than the average contribution of diesel vehicle exhaust (33.4%) during the sampling period. During Asian dust (AD) periods when the air mass passed over the Korean peninsula, Asian dust and secondary organic carbon accounted for 25.2 and 23.0% of the PM_2.5 mass, respectively, whereas Asian dust contributed only 10.8% to the PM_2.5 mass during the AD event when the air mass passed over the Yellow Sea. The contribution of biomass burning to the PM_2.5 mass during the biomass burning (BB) event equaled 63.8%.     

苏州市大气PM_(2.5)中水溶性无机离子的源解析及其气象因子分析

为了探明PM_(2.5)中水溶性无机离子的来源和气象因子对其浓度变化的影响,利用2012年2、5、8和11月苏州市PM_(2.5)中水溶性无机离子浓度和本站气象观测数据,分析了苏州市水溶性无机离子的时间变化特征,解析了当地PM_(2.5)中水溶性无机离子的主要来源,探讨了气象因素对离子组分的影响。结果表明:(1)苏州市PM_(2.5)中水溶性无机离子年均浓度大小依次为:SO_4~(2-)NO_3~-NH_4~+Na~+Cl~-K~+Ca~(2+)Mg~(2+)F~-;SO_4~(2-)、NH_4~+和NO_3~-为PM_(2.5)中最重要的3种水溶性无机离子物种,其总和占PM_(2.5)总质量浓度的50.9%。各离子的季节浓度特征均为冬季最高、夏季最低。(2)通过运用主成分分析法对苏州市PM_(2.5)中水溶性无机离子进行来源分类解析,发现第一类为二次污染源和生物质燃烧,其贡献率为32.84;第二类为道路扬尘及工业排放,其贡献率为19.99%;第三类为海盐污染,其贡献率为18.43%。(3)通过水溶性无机离子与气象条件的相关性分析发现,风向、风速和温度与水溶性无机离子浓度的相关性较显著,这三者是颗粒物浓度变化的主要影响因子。(4)利用HYSPLIT后向轨迹模式对外来污染物进入苏州市的轨迹进行聚类分析后发现:因受季风气候影响,苏州市外来污染物的输入路径存在明显的季节性变化特征,其中夏半年输送主径源自海上,冬半年主径源自内陆。     

流域坡面汇流研究现状述评

对当前坡面汇流计算方法的研究进展进行了较为系统的总结与分析,并对坡面汇流的非线性效应以及城市低影响开发中的雨水入渗与蓄集对坡面汇流的控制作用进行了简要分析。从模型简单实用的角度出发,认为以流域时间-面积关系与线性水库相串联的ModClark法等为代表的概念性分布式坡面汇流模型具有良好的发展前景;考虑到基于等流时单元的变动等流时线法在反映雨强非线性影响中存在的问题,认为根据水文响应单元在不同雨强条件下汇流时间的变化,调整其汇流参数以反映坡面汇流的非线性效应,对于流域坡面汇流的分布式模拟更具有实际意义;针对目前低影响开发设施长时间序列大空间尺度的室外降雨径流监测资料普遍较为缺乏的现状,给出了后期应积极选择合适的技术以加强低影响开发性能监测工作的建议。     

高性能计算技术在气象领域的应用

高性能计算通过应用超级计算机与并行处理技术解决复杂的计算问题,是信息技术发展比较迅猛的领域之一。气象应用始终是高性能计算的重要领域,高性能计算技术有效地解决了高分辨率、高精度气象数值预报模式发展限制,在气象预报预测业务中发挥着核心支撑作用。数十年以来,由于数值天气模式研究和业务运行对计算资源的强烈需求,国内外气象领域高性能计算机及应用迅速发展起来。气象领域对高性能计算能力及系统的可靠性需求日益提升。高性能计算技术将与气象预报应用日益融合,相互影响促进,不断创新发展。为满足气象预报预测业务和科研工作需求,中国气象部门将进一步提升高性能计算能力,并致力于优化集约高性能计算系统布局,高效管理计算资源,发挥最大效益。