基于混沌吸引子的复杂农业系统预测模型研究

农业生产是自然再生产和经济再生产有机结合的过程，农业系统是高度耦合了自然生态、社会经济系统等的复杂大系统，具有非线性和耗散性特征。复杂农业系统要素非线性作用所表现的系统行为形成农业系统的整个演化轨迹。从长期演化来看，由于系统内部自组织作用、系统协同和外部随机扰动的作用使得农业系统出现混沌现象，故复杂系统具有长期不可预测性。本文根据复杂农业系统的预测规范，重构复杂农业系统相空间，建立了基于“混沌吸引子”的非线性混沌动力学预测模型，并以成都的相关数据进行分析验证。     

油气微渗漏扩散物理实验的再现及数值模拟

来自油气藏的烃类物质的微渗漏扩散是普遍存在的。这里主要进行油气微渗漏扩散,在计算机上的物理实验再现过程和三维数值模拟。首先把实验提供的稀疏的数据,通过Kriging插值算法计算,得到三维重建中所需具有规则、分布均匀特点的数据,所得效果较好,并且算法编写简单、容易理解。其次,建立一种关于油气微渗漏扩散的三维数学模型,进行数值模拟,较好地仿真了演示油气微渗漏随时间扩散的过程。最后,简单地探讨了虚拟现实建模语言（VRML）在三维可视化中的运用。     

涡北河网区三维地震采集技术

涡北勘探区属于河网地带,难以进行正常的地震施工。为此,本文提出了一种以规则束状观测为基础和特殊观测系统相结合的数据采集方法。该方法适应了这种复杂的地表条件,获得了"空白区"的地震资料,为今后类似地表条件地区的三维地震勘探提供了经验。      

季节内印度洋-西太平洋对流涛动对次季节-季节尺度大气可预报性的影响

利用非线性局部Lyapunov指数和条件非线性局部Lyapunov指数定量估计了季节内印度洋-西太平洋对流涛动（IPCO）和实时多变量Madden-Julian指数（RMM指数）可预报期限,量化了季节内IPCO对S2S尺度大气可预报性的贡献,深入研究了季节内IPCO演变下S2S尺度可预报期限空间分布的变化规律。结果表明：（1）与RMM指数相比,季节内IPCO指数可预报性更强,可预报期限达到31天左右,比RMM指数高出2周以上;（2）印度洋-西太平洋区域S2S尺度大气可预报性最强,可预报期限达到30天以上,其中季节内IPCO是该地区的主要可预报性来源之一,其贡献达到6天,占总可预报期限的25%以上;（3）随着季节内IPCO的演变,印度洋-西太平洋地区S2S尺度大气可预报性有空间结构变化,表现为可预报期限异常的传播和振荡。S2S尺度大气可预报期限正负异常沿季节内IPCO传播路径,一支以赤道中西印度洋为起点北传至印度半岛,一支向东传播,经过海洋性大陆到赤道西太平洋后向北传播,到达日本南部。同时,可预报性异常的传播在在东印度洋和西太平洋表现出反向变化的特征,形成东西两极振荡,当季节内IPCO向正位相发展时,东印度洋具有更强的可预报性,西太平洋具有更弱的可预报性,反之亦然。季节内IPCO的发展（衰退）可使东印度洋（西太平洋）S2S尺度大气可预报性更强,表明模式预报技巧对此具有更大的提升空间。     

H_2O_2增效TiO_2/凹凸棒石光催化体系降解亚甲基蓝

以钛酸四丁酯为前驱体,天然凹凸棒石为载体,采用溶胶凝胶法制备了TiO_2/凹凸棒石复合光催化剂,并用XRD、TEM对其进行表征.以亚甲基蓝染料为模拟污染物,采用300 W汞灯为紫外光源,以光催化实验来评价该催化剂的活性,并研究了H_2O_2的引入对光催化活性的影响.实验结果表明,H_2O_2能显著提高染料的脱色效率:亚甲基蓝的初始浓度为50 mg/L,催化体系为2 mmol/L H_2O_2+0.5 g/L TiO_2/凹凸棒石+UV(紫外线),光催化10 min后其脱色率为95%,相对于单独的0.5 g/L TiO_2/凹凸棒石+UV催化体系,其脱色率提高了约50%.全波段扫描显示,加入H_2O_2后,亚甲基蓝在290 nm对应的苯环吸收蜂急剧下降,665 nm对应的最大吸收峰则近乎消失,且没有新的吸收峰产生.     

Vertical structures of PM10 and PM 2.5 and their dynamical character in low atmosphere in Beijing urban areas

The vertical structures and their dynamical character of PM2.5 and PM10 over Beijing urban areas are revealed using the 1 min mean continuous mass concentration data of PM2.5 and PM10 at 8, 100, and 320 m heights of the meteorological observation tower of 325 m at Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP CAS tower hereafter) on 10―26 August, 2003, as well as the daily mean mass concentration data of PM2.5 and PM10 and the continuous data of CO and NO2 at 8, 100 (low layer), 200 (middle layer), and 320 m (high layer) heights, in combination with the same period meteorological field observation data of the meteorological tower. The vertical distributions of aerosols observed on IAP CAS tower in Beijing can be roughly divided into two patterns: gradually and rapidly decreasing patterns, I.e. The vertical distribution of aerosols in calm weather or on pollution day belongs to the gradually decreasing pattern, while one on clean day or weak cold air day belongs to the rapidly decreasing pattern. The vertical distributive characters of aerosols were closely related with the dynamical/thermal structure and turbulence character of the atmosphere boundary layer. On the clean day, the low layer PM2.5 and PM10 concentrations were close to those at 8 m height, while the concentrations rapidly decreased at the high layer, and their values were only one half of those at 8 m, especially, the concentration of PM2.5 dropped even more. On the clean day, there existed stronger turbulence below 150 m, aerosols were well mixed, but blocked by the more stronger inversion layer aloft, and meanwhile, at various heights, especially in the high layer, the horizontal wind speed was larger, resulting in the rapid decrease of aerosol concentration, I.e. Resulting in the obvious vertical difference of aerosol concentrations between the low and high layers. On the pollution day, the concentrations of PM2.5 and PM10 at the low, middle, and high layers dropped successively by, on average, about 10% for each layer in comparison with those at 8 m height. On pollution days, in company with the low wind speed, there existed two shallow inversion layers in the boundary layer, but aerosols might be, to some extent, mixed below the inversion layer, therefore, on the pollution day the concentrations of PM2.5 and PM10 dropped with height slowly; and the observational results also show that the concentrations at 320 m height were obviously high under SW and SE winds, but at other heights, the concentrations were not correlated with wind directions. The computational results of footprint analysis suggest that this was due to the fact that the 320 m height was impacted by the pollutants transfer of southerly flow from the southern peripheral heavier polluted areas, such as Baoding, and Shijiazhuang of Hebei Province, Tianjin, and Shandong Province, etc., while the low layer was only affected by Beijing's local pollution source. The computational results of power spectra and periods preliminarily reveal that under the condition of calm weather, the periods of PM10 concentration at various heights of the tower were on the order of minutes, while in cases of larger wind speed, the concentrations of PM2.5 and PM10 at 320 m height not only had the short periods of minute-order, but also the longer periods of hour order. Consistent with the conclusion previously drawn by Ding et al., that air pollutants at different heights and at different sites in Beijing had the character of "in-phase" variation, was also observed for the diurnal variation and mean diurnal variation of PM2.5 and PM10 at various heights of the tower in this experiment, again confirming the "in-phase" temporal/spatial distributive character of air pollutants in the urban canopy of Beijing. The gentle double-peak character of the mean diurnal variation of PM2.5 and PM10 was closely related with the evident/similar diurnal variation of turbulent momentum fluxes, sensible heat fluxes, and turbulent kinetic energy at various heights in the urban canopy. Besides, under the condition of calm weather, the concentration of PM2.5 and PM10 declined with height slowly, it was 90% of 8 m concentration at the low layer, a little lesser than 90% at the middle layer, and 80% at the high layer, respectively. Under the condition of weak cold air weather, the concentration remarkably dropped with height, it was 70% of 8 m concentration at the low layer, and 20%―30% at the middle and high layers, especially the concentration of PM2.5 was even lower.     

登陆台风卡努(0515)内核区环流结构特征分析

本文采用地基雷达轨迹显示技术(Ground Based Velocity Track Display,简称GBVTD)反演的雷达风场资料,分析台风卡努(0515)在登陆期间近中心环流结构特征.轴对称环流结构分析表明,登陆前卡努轴对称切向风速最大值出现在眼墙区域2 km高度附近,最大风速半径随高度向外倾斜.轴对称径向入流...     

水库对河流营养盐滞留效应研究进展

综述了国内外水库对河流营养盐滞留效应的主要进展,尽管在大型水库是否显著改变原有河流输送营养盐的问题上存在诸多争议,反映在滞留效率的估算上,存在前后矛盾的结果,但大量的文献仍支持筑坝显著改变了河流原有输送营养盐的特性,并对下游、河口产生深远的影响.本文将"滞留"的主要过程分为泥沙过滤作用和生物过滤作用,并列举了若干经典的估算滞留效率的方法和部分水库对营养盐的滞留效率.     

Uncertainty analysis of CO_2 flux components in subtropical evergreen coniferous plantation

We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different ...     

“6.3”区域致灾雷暴大风形成及维持原因分析

利用商丘和郑州雷达资料,结合地面加密观测等多种资料,分析了2009年6月3日傍晚至次日凌晨,河南商丘、安徽和江苏北部出现的大范围致灾雷暴大风。本文分两个阶段从中尺度环境、风暴结构、风暴与环境相互作用、雷暴间相互作用的角度对商丘风暴的发展、维持及灾害性大风成因进行了深入探讨,得到以下结论:(1)商丘雷暴大风环境类似美国暖季型Derecho环境;(2)商丘风暴由晋冀雷暴群下沉气流导致的出流阵风锋移动到水汽相对充沛处触发,在有利的环境条件下迅速发展成具有较强的中层径向辐合超级单体风暴,多个超级单体的强下沉气流合并产生了超级单体阶段的地面大风;(3)飑线发展、维持的原因是飑线的自组织结构,飑线与环境入流的相互作用既有利于强上升气流发展,亦有利于强下沉气流发展,干线及叠加在干线上扰动触发的新生回波带不断并入飑线北端;(4)根据径向速度增幅估计,风暴强下沉气流辐散、强冷池密度流和层状云部分降水粒子蒸发对弓形回波阶段地面灾害性大风的增幅作用几乎相当,冷池合并是商丘极端雷暴大风产生的重要原因。