基于聚类算法的钻孔瞬变电磁视电阻率立体成像方法

煤矿井下地球物理水害超前探测要求探测点20 m范围内不得有积水和金属物体,传统电磁法超前探测技术已不能满足要求,钻孔瞬变电磁法通过将收发装置送入掘进工作面前方的钻孔中进行探测,既远离了巷道中的各种干扰,又提高了隐蔽致灾水体的探测精度。为解决该方法对钻孔径向异常体的准确定位解释难题,通过三维正演总结了其水平分量异常响应特征,提出了异常体象限确定准则,研究了根据水平分量幅值和异常象限综合求取异常体工具面角的计算方法。将由垂直分量计算得到的每一个视电阻率视为独立异常体,基于K-means聚类算法对相应的水平分量异常曲线特征值进行二分类,实现了全数据集的视电阻率象限自动划分,结合异常工具面角算法研究得出钻孔瞬变电磁视电阻率立体成像方法。最后计算了三维数值模型的立体成像结果,对钻孔径向的小规模低阻异常体取得了良好效果。结果表明:基于K-means聚类算法的钻孔瞬变电磁视电阻率立体成像方法是地球物理与机器学习的有机结合,该方法能够为井下掘进工作面隐伏水害超前探测精细解释提供技术支撑。     

基于半监督核模糊c-均值算法的北京一号小卫星多光谱图像分类

针对遥感图像数据大多不服从高斯分布以及遥感图像分类存在非线性、模糊性和标记数据少等问题,提出基于半监督核模糊c-均值算法的多光谱遥感图像分类方法.首先,把半监督学习理论和核理论同时引入模糊c-均值算法,形成半监督核模糊c-均值算法.然后,用该算法与k-均值算法、最大似然算法、多类支持向量、半监督核支持向量、模糊c-均值...     

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.     

用统计集合方法制作全国汛期降水滚动预测试验

以全国160站汛期(6-8月)降水量为预测量,以最新得到的74项环流特征量指数为因子,尝试制作全国160站汛期降水滚动预测.建立模型时考虑了预测量与环流特征量因子序列的显著线性变化趋势,以及预测量与环流因子之间的相关不稳定性,用"滑动相关-逐步回归-集合分析"预测方法,分别建立了2009年全国160站汛期降水量的物理统计集合分析预测模型,并进行了近10年独立样本预测试验分析.结果显示:(1)用物理统计集合分析预测方法,以最新得到的74项环流特征量指数为因子,实现了全国160站汛期降水逐月滚动预测,2009年以在5月份制作的滚动预测效果最好.(2)近10年预测试验的空间距平相关系数Acc、业务评分PS和异常级评分TS均高于国家气候中心近年汛期预测业务平均水平.经过不断改进思路和优化具体建模方案,该方法具有较高的业务应用潜力.     

以用户为导向的交互式预报系统及应用研究

基于近年来涌现的有关新一代气象预报系统发展的认识,本文提出用户导向的交互式预报系统的概念模型。新系统强调在用户信息分析的基础上,发展从用户出发再回到用户不断自我改善的预报流程。系统组成的关键模块包括：用户端风险决策动态需求分析模块、物理预测模块、用户目标量的降尺度模块,用户端专业耦合模块及用户风险决策模块。文中阐述了系统中各模块间的联系、用户端信息在系统中的反馈作用以及具体的“交互式”方式。以临沂地区水文用户为例,以引发洪涝的降水事件为预报对象,利用TIGGE全球超集合预报,初步构建了一个临沂水文用户导向的可能致洪降水交互式预报系统。这个具备迭代式自我完善功能的新型预报系统包含了汛期随时变化的用户决策信息、由前期影响雨量和当前水文条件决定的可变致洪降水阈值以及一个动态的用户端预报水平和不确定性评估模块。初步结果表明,结合用户端信息的预报优于未考虑用户信息的预报结果,从而更直接地帮助用户进行防汛决策。个例研究也为发展更完整的用户导向预报系统提供了参考。     

The social and political complexities of learning in carbon capture and storage demonstration projects

Demonstration of a fully integrated power plant with carbon capture and storage (CCS) at scale has not yet been achieved, despite growing international political interest in the potential of the technology to contribute to climate change mitigation and calls from multiple constituents for more demonstration projects. Acknowledging the scale of learning that still must occur for the technology to advance towards deployment, multiple CCS demonstration projects of various scales are emerging globally. Current plans for learning and knowledge sharing associated with demonstration projects, however, seem to be limited and narrowly conceived, raising questions about whether the projects will deliver on the expectations raised. Through a comparison of the structure, framing and socio-political context of three very different CCS demonstration projects in different places and contexts, this paper explores the complexity of social learning associated with demonstration projects. Variety in expectations of the demonstration projects’ objectives, learning processes, information sharing mechanisms, public engagement initiatives, financing and collaborative partnerships are highlighted. The comparison shows that multiple factors including the process of building support for the project, the governance context and the framing of the project matter for the learning in demonstration projects. This analysis supports a broader conceptualization of learning than that currently found in CCS demonstration plans - a result with implications for both future research and practice.     

Co-management and the co-production of knowledge: Learning to adapt in Canada's Arctic

Co-management institutional arrangements have an important role in creating conditions for social learning and adaptation in a rapidly changing Arctic environment, although how that works in practice has not been clearly articulated. This paper draws on three co-management cases from the Canadian Arctic to examine the role of knowledge co-production as an institutional trigger or mechanism to enable learning and adapting. Experience with knowledge co-production across the three cases is variable but outcomes illustrate how co-management actors are learning to learn through uncertainty and environmental change, or learning to be adaptive. Policy implications of this analysis are highlighted and include the importance of a long-term commitment to institution building, an enabling policy environment to sustain difficult social processes associated with knowledge co-production, and the value of diverse modes of communication, deliberation and social interaction.     

初始场的同化对短期气候预测的影响

基于已建立的三维变分资料同化系统(3D-VAR),利用大气环流模式(IAP9L2°×2.5°-AGCM),对同化和未同化2种初始场分别进行了17a(1988—2004年)的集合回报试验,并对试验结果进行了相关分析.结果表明:在热带地区,2组初始场下的集合回报结果差别很小,除热带外的中高纬地区差别较大,尤其是东亚地区;另外,大部分物理量场的17a异常空间相关系数的均值在同化后的初始场下也得到了提高,可能是因为同化的初始场包含了一段时间的大气信息,动力模式更加协调.     

《国际汉语能力标准》中值得商榷的地方——兼与《欧洲语言共同参考框架:学习、教学、评估》比较

通过对《国际汉语能力标准》的考察,同时与《欧洲语言共同参考框架:学习、教学、评估》比较,发现《国际汉语能力标准》存在三个方面的问题:结构不合理、内容有缺失以及评级等级标准不科学,以此研究结果为《国际汉语能力标准》的修订提供参考。     

广东“18.8”季风槽极端降水事件对流尺度集合预报分析

受季风槽影响,2018年8月30—31日华南地区出现一次极端暴雨过程,单日站点累计降水量达1?056.7 mm,刷新了广东有历史纪录以来新的极值。对于此次极端降水事件,常用的业务模式包括欧洲中期天气预报中心全球模式（ECMWF）、日本气象厅谱模式（JMA）和中国气象局广东快速更新同化数值预报系统（CMA-GD）,都低估了降水强度。利用深圳市气象局业务对流尺度集合预报系统分析了此次特大暴雨过程,结果表明：对流尺度集合预报系统对本次特大暴雨过程具有比较好的预报能力,概率匹配平均最大雨量达348.7 mm·(24 h)-1,集合平均的强降水中心和观测基本一致,观测极值附近区域发生大暴雨（≥150 mm）概率最大值达到80%。选取了较“好”和较“差”集合成员预报进行对比分析,发现较“好”成员预报的强降水中心位置和观测基本一致,而较“差”成员预报的降水中心位置则偏向福建地区。较 “好”成员预报出莲花山南侧地面中尺度辐合线较长时间的维持和缓慢移动,导致强降水雨团在莲花山脉附近不断地触发和维持,同时地形的阻挡作用使得对流系统在地形附近区域持续维持,造成了罕见的特大暴雨;而较“差”成员辐合区位于莲花山以北,对流形成后向东、向北移动,最终导致强降水预报位置偏向福建地区。