重庆多历时极值降水的概率型优选及应用

基于重庆34个国家气象站1981—2016年5 min,24 h至3 d的过程最大雨量共18个年极值降水序列,利用线性矩法计算6种常用概率分布函数的模型参数,通过备选最优模型筛选法客观选取各站各历时极值降水的最优概率模型,并将优选结果应用于重庆不同历时百年重现期降水的计算。结果表明:模型优选方法得到的重庆各站不同历时极值降水的最优线型略有差异,广义极值分布占比最大,三参数Weibull分布次之,三参数对数正态分布第三,皮尔逊Ⅲ型和Gumbel分布相当,指数分布最差。最优线型计算的重庆不同历时百年重现期降水的空间分布大值区由短历时的点状分布向长历时的片状分布转变,渝东北的大值中心受地形影响不断向北移动。基于线性矩法的概率模型参数估计及客观的线型优选过程具有较强的可操作性和适应性,可应用于其他工程气象参数的推算中。     

我国中部地区夏季干旱的多时间尺度特征及其环流、海温异常

基于CRU逐月降水和NCEP/NCAR再分析等资料,利用集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)方法,分析1948—2016年中国中部地区夏季降水变化的多时间尺度特征及其对应的环流、海温异常,进而解释不同时期影响干旱发生的背景场有何不同。结果表明,中部地区夏季降水以年际变化为主,周期长度为3.8 a和6.9 a,年代际和多年代际变化的方差贡献不足20%。然而,各时间尺度降水变化对不同时期干旱事件的贡献存在较大差异,1960s、1970s,降水年际变化偏弱,相反地,多年代际变化正处于负位相的极小值期;1980s、1990s,多年代际变化位相转正;2000s初,年际变化明显增强。此外,通过分析不同时间尺度降水变化对应的环流、海温背景场,发现热带印度洋海温异常及其引起的西北太平洋副热带高压的变化、大西洋北部海温异常激发的纬向波列以及贝加尔湖地区的阻塞活动、1970s末PDO位相转变伴随的东亚夏季风突变是分别解释降水年际、年代际和多年代际变化的主要原因,进而揭示影响中部地区夏季干旱发生的关键因子及其相对重要性。     

A three-tiered approach to participatory vulnerability assessment in the Solomon Islands

Greater recognition of the seriousness of global environmental change has led to an increase in research that assesses the vulnerability of households, communities and regions to changing environmental or economic conditions. So far, however, there has been relatively little attention given to how assessments can be conducted in ways that help build capacity for local communities to understand and find their own solutions to their problems. This paper reports on an approach that was designed and used to work with a local grass roots organization in the Solomon Islands to promote inclusivity and participation in decision-making and to build the capacity of the organization to reduce the vulnerability of communities to drivers of change. The process involved working collaboratively with the organization and training its members to conduct vulnerability assessments with communities using participatory and deliberative methods. To make best use of the learning opportunities provided by the research process, specific periods for formal reflection were incorporated for the three key stakeholders involved: the primary researchers; research assistants; and community members. Overall, the approach: (1) promoted learning about the current situation in Kahua and encouraged deeper analysis of problems; (2) built capacity for communities to manage the challenges they were facing; and (3) fostered local ownership and responsibility for problems and set precedents for future participation in decision-making. While the local organization and the communities it serves still face significant challenges, the research approach set the scene for greater local participation and effort to maintain and enhance livelihoods and wellbeing. The outcomes highlight the need for greater emphasis on embedding participatory approaches in vulnerability assessments for communities to benefit fully from the process.     

基于深度学习的天气雷达异常数据识别技术

天气雷达基数据中因观测设备故障或标定问题而产生的异常数据, 直接影响天气雷达数据质量、定量估测降水及天气系统的分析和判断。目前在中国气象局气象探测中心实时业务中, 通过人工勘误环节对异常数据进行处理。针对2020—2022年业务中勘误较多的、大面积故障异常和易与降水数据混合的局部电磁干扰或故障的两类异常数据, 分别构建和训练R-ResNet和R-LinkNet两种模型, 提取雷达硬件故障、电磁干扰等特征, 实现异常数据的识别和处理。评估结果表明:两种模型在提取异常数据特征方面均具有很强的学习能力, R-ResNet在分类判识异常数据与正常数据的准确率超过99%, R-LinkNet在分离电磁干扰杂波和降水回波的准确率超过98%。两种模型可用于实时业务中监控和勘误电磁干扰、故障等异常数据, 实现异常数据的自动勘误处理。     

基于机器学习的重庆长江航道雾图像特征识别研究

基于重庆市境内长江航道雷达站拍摄的雾天气过程影像资料,利用K最近邻、支持向量机、BP神经网络、随机森林等机器学习算法,对无雾和5类有雾天气个例进行图像识别训练,构建雾图像识别模型,并检验了识别准确率。结果表明：机器学习能够有效识别雾图像,随机森林算法的识别效果优于其余3种算法。对于能见度超过1500 m的无雾天气,模型的识别准确率为100%,对于能见度在1000—1500 m范围内的轻雾、能见度低于50 m的强浓雾,模型的识别准确率在90%以上,对于能见度在50—1000 m范围内的雾、大雾和浓雾,识别准确率超过70%。     

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.     

A conceptual framework for analysing adaptive capacity and multi-level learning processes in resource governance regimes

Governance failures are at the origin of many resource management problems. In particular climate change and the concomitant increase of extreme weather events has exposed the inability of current governance regimes to deal with present and future challenges. Still our knowledge about resource governance regimes and how they change is quite limited. This paper develops a conceptual framework addressing the dynamics and adaptive capacity of resource governance regimes as multi-level learning processes. The influence of formal and informal institutions, the role of state and non-state actors, the nature of multi-level interactions and the relative importance of bureaucratic hierarchies, markets and networks are identified as major structural characteristics of governance regimes. Change is conceptualized as social and societal learning that proceeds in a stepwise fashion moving from single to double to triple loop learning. Informal networks are considered to play a crucial role in such learning processes. The framework supports flexible and context sensitive analysis without being case study specific.First empirical evidence from water governance supports the assumptions made on the dynamics of governance regimes and the usefulness of the chosen approach. More complex and diverse governance regimes have a higher adaptive capacity. However, it is still an open question how to overcome the state of single-loop learning that seem to characterize many attempts to adapt to climate change. Only further development and application of shared conceptual frameworks taking into account the real complexity of governance regimes can generate the knowledge base needed to advance current understanding to a state that allows giving meaningful policy advice.     

Climatology of Shear Line and Related Rainstorm over the Southern Yangtze River Valley Based on an Improved Intelligent Identification Method

Based on four reanalysis datasets including CMA-RA, ERA5, ERA-Interim, and FNL, this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear. Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley(SYRV) during the summers(June-August) from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm,namely the t-distributed stochastic neighbor embeddin...     

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.     

Student Perceptions of a Recreation Trail Assignment as a Valuable Learning Experience for Geography Undergraduates

Charging undergraduate geography students with the task of designing a recreational trail in their local community offers an engaging experiential opportunity with potential to advance geographic learning in a real-world setting. This article presents an assignment in which students were asked to develop a recreational trail proposal for an undeveloped local conservation area and the results of a survey that asked the students to reflect and report upon the educational value of this experience one year later. Results of the survey validated the assignment's lasting value to the students across the cognitive, psychomotor, and affective learning domains.