四川盆地云和降水观测科学试验设计与实践

2014—2017年四川地区开展了大范围云系观测科学试验。观测对象以盆地层状云系和积层混合云系为主,积状云(对流云)为辅。本文围绕试验目标、区域、观测要素、观测布局设计、观测方案设计、设备技术参数和典型个例等7个方面进行介绍,根据不同类型云系和降水变化特征,设计有针对性的观测方案,获得了不同类型云系和降水的多尺度连续性观测数据,为四川地区开展云和降水关系研究提供详实的综合型外场观测数据。层状降水云典型个例云高可达8km,云强核心部位的云雷达反射率可达28dBz,径向速度可达-6m·s~(-1),在0℃附近,反射率和退偏振因子LDR上有一条明显的亮带,表现为极大值,液态水主要集中于4.5km以下,随着雨强增大,液水含量增加,降水滴谱分布较窄,随着雨强减小,雨滴谱和速度谱变窄,但小粒径数浓度增加,说明对层状云雨强起主导作用的是雨滴直径,而不是数浓度;无降水层状云典型个例云层厚度为3.2km,云顶约为4km,云雷达反射率不超过0dBz,径向速度不超过5m·s~(-1),层状云内整层水汽含量和液水含量较为稳定,云中主要为液态粒子且粒径偏小、小粒径数浓度较高。     

具有随机扰动和混合时滞的两层异质网络的同步研究

在现实生活中,很多复杂的系统不是由单个网络来表示,而是由一组相互依赖的网络系统来表示的.本文研究了具有随机扰动和混合时滞的两层异质网络的对应节点的同步控制问题.基于随机微分时滞方程的LaSalle不变原理和Lyapunov稳定性理论,采用牵制控制方法,对部分节点实施控制,给出了实现同步的充分条件.为了降低反馈控制的增益,结合自适应控制的方法,进一步弱化了两层异质网络实现同步的条件.最后,通过数值仿真,验证了理论结果的有效性.     

超参数对GRU-CNN混合深度学习弹性阻抗反演影响研究

CNN-GRU混合深度学习反演弹性阻抗取得了较好的反演效果.但是,基于深度学习的叠前反演参数众多,包括内部深度学习网络可学习参数和外部超参数等,目前超参数选取对网络性能及计算速度影响尚缺乏系统性研究,这直接影响到了该方法的进一步推广应用.因此,本文在混合深度学习反演弹性阻抗基础上,探讨学习率、Ep-och、batch_...     

街头抢劫者前犯罪经历对其后作案地选择的影响

作案地选择是犯罪地理学的研究主题。已有的重复作案地选择的研究表明,犯罪者“前案件”作案地选择对他们“后案件”作案地选择具有影响,但以往研究关注的是先前的犯罪时间和地点对其后续作案地选择的影响,仍未检验犯罪者在“前案件”中犯罪经历的具体作用。因此,论文以中国东南沿海ZG市为例,利用街头抢劫者的抓捕数据和混合Logit模型,聚焦探析街头抢劫者先前的个体犯罪经历对他们随后的作案地选择的影响。研究发现：街头抢劫者在“前案件”中的犯罪间隔、犯罪出行和当场被捕等个体犯罪经历对其“后案件”作案地选择具有强烈的影响,即“前后案件”的犯罪间隔越临近、“前案件”犯罪出行距离越短,以及“前案件”未当场被捕,则大大增加了街头抢劫者返回到先前抢劫区域再次犯罪的可能性。并通过警察访谈和结合理论分析,发现“前案件”未当场被捕是由犯罪者当场被捕的恐惧感、警察特殊的干预方式,以及社会凝聚力和犯罪防控的相互作用而形成。研究结论可为警务部门的“事前防控”与“主动处置”提供一定的参考。     

结合BM3D和多级非线性加权平均中值滤波的遥感影像混合噪声去噪方法

提出了一种基于三维块匹配（BM3D）和多级非线性加权平均中值滤波的遥感影像混合噪声去噪方法,使用coief3小波和sym2小波替换传统三维块匹配算法中的bior1.5和hear参数,将三维块匹配算法和多级非线性加权平均中值滤波相结合以提高算法对混合噪声的去噪能力。进行一个仿真对比试验和一个真实遥感影像去噪试验,试验表明,本文提出的方法在遥感影像混合噪声去噪方面效果要优于传统混合噪声去噪方法。     

加权混合估计中权值的确定方法

综合了大地测量中各种异方差多源观测模型和联合平差方法,说明了混合估计方法可以用于测量数据融合,平衡附加信息和样本信息对参数估计的影响。通过求取权值使参数估计的协方差阵的迹最小的方法,给出了一个权的最优选择方法。本文扩展了已有的加权混合估计方法,使得新方法中的权不受验前单位权方差的限制,能有效应用于大地测量数据处理。     

The impact of northern Indian Ocean rivers on the Bay of Bengal using NEMO global ocean model

The effect of river runoff over the northern Indian Ocean(NIO) especially over the Bay of Bengal(Bo B) has been studied using global Nucleus for European Modelling of the Ocean(NEMO). Two sensitivity experiments, with and without river runoff are conducted and the influence of river runoff on the Indian Ocean hydrography,stratification and circulation features are studied. It is found that due to river runoff surface salinity over the northern Bo B decreases by more than 5 and the East India Coastal Current strengthens by 2 cm/s during post monsoon season. The fresh river water reaches up to 15°N in the Bo B and is the main cause for low salinity there.Sea surface temperature in the northwestern Bo B increases by more than 0.2℃ due to the river runoff in summer monsoon while surface cooling upto 0.2℃ is seen in north-west part of Bo B in winter season. The seasonal mixed layer depth in the region is found to be dependent on river runoff. The effect of vertical shear and Brunt Vaisala frequency on stratification is also examined. The ocean water becomes highly stratified up to 3 035 m due to the river runoff. It is found that the energy required for mixing is high in the northern and coastal Bo B.     

城市居民日常身体活动时空分异特征及影响因素

依托南京主城区居民日常身体活动调查数据,基于身体活动时空维度,挖掘城市居民日常身体活动时空分异特征,并采用混合效应模型探究身体活动分异的影响因素。研究表明：① 居民日常身体活动时空异质特征明显。工作日/非工作日不同类型身体活动时间安排及个体间差异均显著;工作性、交通性、家务性身体活动空间制约明显,而休闲性身体活动空间分布更具弹性;与工作日相比,非工作日不同类型身体活动空间范围变化收敛与扩散特征并存。② 活动空间范围、个人社会经济属性、自身健康状况对居民日常身体活动分异均具有显著影响。具体而言,居民日常活动空间范围决定身体活动的空间适应与选择;社会分工差异导致不同性别、年龄人群身体活动具有指向性;身体活动时间出现与个人社会经济实力倒置现象;而良好健康状态会激励居民日常身体活动的保持。     

结合空间信息选取最优端元组合的混合像元分解

传统的混合像元分解算法认为每个像元都包含图像中所能提取的全部端元组分,但这并不符合实际情况。实际上图像中大多数混合像元仅由少部分端元混合而成。由于端元提取精度及噪声的影响,采用全部端元对混合像元进行分解,会使得混合像元中实际并不存在的端元的丰度估计值不为零,分解结果存在较大误差。由于混合像元大多存在于不同地物的交界处,基于此,本文提出了一种结合图像的空间信息选取混合像元最优端元子集的方法。利用一个空间结构元素,从混合像元的附近邻域开始搜索,将搜索到的纯净像元光谱与所提取的图像端元光谱进行对比,并确定混合像元的端元子集进行分解。根据RMSE大小和变化情况,逐步扩大结构元素的大小,不断调整搜索范围,直至得到最优端元组合。模拟数据和真实数据的试验结果表明,该方法相比传统的全端元光谱分解方法,在总体上获得了更好的分解效果。     

Upper ocean high resolution regional modeling of the Arabian Sea and Bay of Bengal

In this paper, effort is made to demonstrate the quality of high-resolution regional ocean circulation model in realistically simulating the circulation and variability properties of the northern Indian Ocean(10°S–25°N,45°–100°E) covering the Arabian Sea(AS) and Bay of Bengal(BoB). The model run using the open boundary conditions is carried out at 10 km horizontal resolution and highest vertical resolution of 2 m in the upper ocean.The surface and sub-surface structure of hydrographic variables(temperature and salinity) and currents is compared against the observations during 1998–2014(17 years). In particular, the seasonal variability of the sea surface temperature, sea surface salinity, and surface currents over the model domain is studied. The highresolution model's ability in correct estimation of the spatio-temporal mixed layer depth(MLD) variability of the AS and BoB is also shown. The lowest MLD values are observed during spring(March-April-May) and highest during winter(December-January-February) seasons. The maximum MLD in the AS(BoB) during December to February reaches 150 m (67 m). On the other hand, the minimum MLD in these regions during March-April-May becomes as low as 11–12 m. The influence of wind stress, net heat flux and freshwater flux on the seasonal variability of the MLD is discussed. The physical processes controlling the seasonal cycle of sea surface temperature are investigated by carrying out mixed layer heat budget analysis. It is found that air-sea fluxes play a dominant role in the seasonal evolution of sea surface temperature of the northern Indian Ocean and the contribution of horizontal advection, vertical entrainment and diffusion processes is small. The upper ocean zonal and meridional volume transport across different sections in the AS and BoB is also computed. The seasonal variability of the transports is studied in the context of monsoonal currents.