三个文蛤家系双列杂交设计在两种环境下的配合力及杂种优势分析

A 3×3 complete diallel cross comprising three families of the clam Meretrix meretrix(P1, P2 and P3) was used to determine the combining ability of parental families and heterosis of F1 under indoor and openair environments for growth traits. Analysis of variance for shell length and whole body weight indicated highly significant cross effects, environment effects and the interaction of cross by environment. General combining ability(GCA) and specific combing ability exhibited great variation among crosses and between two environments. Pooled over environments, P2 was the top combiner among the three parental families for both traits studied. The cross of P1 and P3 had the highest SCA. Additionally, significant reciprocal effects were observed. For individual environment, about half of the crossbred combinations showed favorable Mid-parent heterosis(MPH)(1%) for the shell length and whole body weight. Our data has shown that non-additive genetic and reciprocal effects constituted the major sources of genetic variation for both shell length and whole body weight, which indicates that crossbreeding among selective families could further explore the heterotic effects.     

基于城市内涝仿真模型的天津风暴潮灾害评估

基于城市内涝仿真模型,根据天津沿海地区的地形、地貌特征以及排水系统等对城市内涝仿真模型进行改进,在沿海边界和河口设置时变水位,使得模型拓展到既能模拟暴雨产生的内涝,也能模拟由于风暴潮侵袭造成的淹没情景。该模型对天津沿海地区历史上典型风暴潮个例以及10年、20年、50年、100年一遇重现期风暴潮产生的积水范围和积水深度进行了模拟,并对2012年8月3日台风达维 (1210) 造成的天津沿海风暴潮进行了业务试应用。将历史风暴潮个例模拟结果以及2012年8月3日的评估结果与实际灾情进行对比,结果显示模型具有较好的模拟能力,可应用于风暴潮灾害的评估和预估业务中,为相关部门和行业提供决策参考。     

基于MapReduce计算模型的气象资料处理调优试验

云计算技术使用分布式的计算技术实现了并行计算的计算能力和计算效率,解决了单机服务器计算能力低的问题。基于长序列历史资料所计算得出的气候标准值对于气象领域实时业务、准实时业务及科学研究中均具有重要的意义。由于长序列历史资料数据量大、运算逻辑较复杂,在传统单节点计算平台上进行整编计算耗时非常长。该文基于Hadoop分布式计算框架搭建了集群模式的云计算平台,以长序列历史资料作为源数据,基于MapReduce计算模型实现了部分整编算法,提高计算时效。同时,由于数据源本身具有文件个数多、单个文件小等特点,对数据源存储形式及数据文件大小进行改造,分别利用SequenceFile方式及文本文件合并方式对同一种场景进行计算时效对比测试,分别测试了10个文件合并、100个文件合并两种情况,使时效性得到了更大程度的提升。     

Climatological study of ionospheric irregularities over the European mid-latitude sector with GPS

High-frequency variability of the ionosphere, or irregularities, constitutes the main threat for real-time precise positioning techniques based on Global Navigation Satellite Systems (GNSS) measurements. Indeed, during periods of enhanced ionospheric variability, GNSS users in the field—who cannot verify the integrity of their measurements—will experience positioning errors that can reach several decimeters, while the nominal accuracy of the technique is cm-level. In the frame of this paper, a climatological analysis of irregularities over the European mid-latitude region is presented. Based on a 10 years GPS dataset over Belgium, the work analyzes the occurrence rate (as a function of the solar cycle, season and local time) as well as the amplitude of ionospheric irregularities observed at a single GPS station. The study covers irregularities either due to space weather events (solar origin) or of terrestrial origin. If space weather irregularities are responsible for the largest effects in terms of ionospheric error, their occurrence rate highly depends on solar activity. Indeed, the occurrence rate of ionospheric irregularities is about 9 % during solar maximum, whereas it drops to about 0 % during medium or low solar activity periods. Medium-scale ionospheric disturbances (MSTIDs) occurring during daytime in autumn/winter are the most recurrent pattern of the time series, with yearly proportions slightly varying with the solar cycle and an amplitude of about 10 % of the TEC background. Another recurrent irregularity type, though less frequent than MSTIDs, is the noise-like variability in TEC observed during summer nighttime, under quiet geomagnetic conditions. These summer nighttime irregularities exhibit amplitudes ranging between 8 and 15 % of the TEC background.     

3S技术在辽宁省海岛地名普查中的应用

3S技术因具有快速、准确、实时的导航定位、对地实测和强大的空间信息加工、处理和分析能力,在国防、气象、灾害监测与环境保护、能源、交通、土地资源勘察等领域得以广泛应用。辽宁省海岛地名普查首次采用了3S技术,实现了海岛地名信息和海岛地理基础数据调查与管理的一体化,达到了海岛地理实体、数据信息、外业调查照片的一致性,建立了海岛地名信息系统。详细介绍了辽宁省海岛地名调查中,3S技术在数字化、导航定位和信息识别、提取、勘测、分析、处理以及数据集成等环节中的应用。     

The roles of different mechanisms related to the tide-induced fronts in the Yellow Sea in summer

In summer, the Yellow Sea Cold Water Mass （YSCWM） is a stable water mass of low temperature lying at the bottom of the central Yellow Sea （YS）. It is fringed by some typical tidal fronts, which separate deep, stratified water on the offshore side from the well-mixed, shallow water on the inshore side. Three striking fronts--Subei Bank Front （SBF）, Shandong Peninsula Front （SPF）, and Mokpo Front （MKF; a front off the southwestern tip of the Korean Peninsula）--have been iden- tified by various studies from both satellite observations and model results. Tide plays an important role in the formation and maintenance of these fronts. However, it is still a matter of debate as to the roles these two kinds of mechanisms of upwelling and tidal mixing play, and how importance they are in the maintenance processes of the above three fronts. Basing a nested high-resolution model HYCOM （the Hybrid Coordinate Ocean Model）, this study focuses on the different mechanisms of tidal effects on the thermal fronts in the YS in summertime. Through comparative experiments with and without tidal forcing, the results indicate that the MKF is mainly driven by tide-induced upwelling. For the SPF, tidal mixing is the dominant factor, when lower cold water is stirred upwards along the sloping topography of the western YS. Meanwhile, the combined effect of upwelling and tidal mixing is the main cause of the formation of the SBF. Diagnostic analysis of thermal balance shows that horizontal nonlinear advection induced by strong tidal currents also contributes to the thermal balance of frontal areas.     

A copula-based multivariate analysis of Canadian RCM projected changes to flood characteristics for northeastern Canada

In the present work, climate change impacts on three spring (March–June) flood characteristics, i.e. peak, volume and duration, for 21 northeast Canadian basins are evaluated, based on Canadian regional climate model (CRCM) simulations. Conventional univariate frequency analysis for each flood characteristic and copula based bivariate frequency analysis for mutually correlated pairs of flood characteristics (i.e. peak–volume, peak–duration and volume–duration) are carried out. While univariate analysis is focused on return levels of selected return periods (5-, 20- and 50-year), the bivariate analysis is focused on the joint occurrence probabilities P1 and P2 of the three pairs of flood characteristics, where P1 is the probability of any one characteristic in a pair exceeding its threshold and P2 is the probability of both characteristics in a pair exceeding their respective thresholds at the same time. The performance of CRCM is assessed by comparing ERA40 (the European Centre for Medium-Range Weather Forecasts 40-year reanalysis) driven CRCM simulated flood statistics and univariate and bivariate frequency analysis results for the current 1970–1999 period with those observed at selected 16 gauging stations for the same time period. The Generalized Extreme Value distribution is selected as the marginal distribution for flood characteristics and the Clayton copula for developing bivariate distribution functions. The CRCM performs well in simulating mean, standard deviation, and 5-, 20- and 50-year return levels of flood characteristics. The joint occurrence probabilities are also simulated well by the CRCM. A five-member ensemble of the CRCM simulated streamflow for the current (1970–1999) and future (2041–2070) periods, driven by five different members of a Canadian Global Climate Model ensemble, are used in the assessment of projected changes, where future simulations correspond to A2 scenario. The results of projected changes, in general, indicate increases in the marginal values, i.e. return levels of flood characteristics, and the joint occurrence probabilities P1 and P2. It is found that the future marginal values of flood characteristics and P1 and P2 values corresponding to longer return periods will be affected more by anthropogenic climate change than those corresponding to shorter return periods but the former ones are subjected to higher uncertainties.     

Present and projected degree days in China from observation,reanalysis and simulations

Degree days are usually defined as the accumulated daily mean temperature varying with the base temperature, and are one of the most important indicators of climate changes. In this study, the present-day and projected changes of four degree days indices from daily mean surface air temperature output simulated by Max Planck Institute, Earth Systems Model of low resolution (MPI-ESM-LR) model are evaluated with the high resolution gridded-observation dataset and two modern reanalyses in China. During 1979–2005, the heating degree days (HDD) and the numbers of HDD (NHDD) have decreased for observation, reanalyses (ERA-Interim and NCEP/NCAR) and model simulations (historical and decadal experiments), consistent with the increasing cooling degree days (CDD) and the numbers of CDD (NCDD). These changes reflect the general warming in China during the past decades. In most cases, ERA-Interim is closer to observation than NCEP/NCAR and model simulations. There are discrepancies between observation, reanalyses and model simulations in the spatial patterns and regional means. The decadal hindcast/forecast simulation performance of MPI-ESM-LR produce warmer than the observed mean temperature in China during the entire period, and the hindcasts forecast a trend lower than the observed. Under different representative concentration pathway (RCP) emissions scenarios, HDD and NHDD show significant decreases, and CDD and NCDD consistently increase during 2006–2100 under RCP8.5, RCP4.5 and RCP2.6, especially before the mid-21 century. More pronounced changes occur under RCP8.5, which is associated with a high rate of radiative forcing. The 20th century runs reflect the sensitivity to the initial conditions, and the uncertainties in terms of the inter-ensemble are small, whereas the long-term trend is well represented with no differences among ensembles.     

多源道路智能选取的本体知识推理方法EI北大核心CSCD

大数据时代道路数据来源日益增多,跨数据源的道路选取面临巨大挑战。本文针对数据语义不一致问题,提出一种基于本体知识推理的多源道路选取方法。首先,将1∶5万基本比例尺地形图道路数据作为基础案例,将四维图新导航电子地图和开放街道地图中的道路数据作为试验数据,基于stroke计算道路等级、长度、连通度、接近度、中介度特征项,提取特征项概念并构建本体;然后,从语义特征项和数值特征项两方面计算本体概念相似性,建立基础案例与试验数据间的关联关系;最后,基于本体和语义网规则语言定义本体通用、语义特征、数值特征三类选取规则,实现跨数据源道路选取的过程性知识推理。试验表明,本文方法可基于本体概念相似性度量消除语义差异,同时利用语义网规则语言进行知识推理,可实现多源道路数据向基本比例尺数据的智能选取。     

深度学习支持下的地图图片典型地理目标检测

针对地图图片中典型地理目标识别问题,本文首先介绍了两种基于深度学习的目标检测方法(YOLO网络和采用focal loss替换交叉熵损失函数的RetinaNet网络),然后将地图图片分别输入两种神经网络模型中进行训练和测试,最后对目标检测结果进行对比分析。结果表明,RetinaNet网络模型对地图图片进行目标检测的准确率有明显提高,且运行速度依然可达秒级。该地理目标检测方法的高准确度与高效性可在地图审查时节约大量人力、时间成本,为地图内容智能理解及互联网地图监管提供了新的技术参考。