浙江雁荡山火山-侵入杂岩的岩浆混合作用——暗色包体中长石环带的证据

浙江雁荡山是中国东南部燕山晚期巨型火山-侵入杂岩带的重要组成部分。对其中央侵入相石英正长斑岩的暗色微粒包体中的斑晶和基质斜长石进行了详细的内部结构和成分分析,揭示了斜长石复杂环带的成因和相关的岩浆作用过程。斑晶斜长石由熔蚀的核部和表面干净的幔部组成,边部包裹有钾长石膜。核部斜长石呈浑圆状或港湾状,内部发育筛状结构,An成分显著低于幔部斜长石,代表来自酸性岩浆房中早期结晶的斜长石捕掳晶。同时,幔部斜长石与自形、表面干净的基质斜长石具有类似的An含量,且两者均含有针状磷灰石的包裹体,应结晶自与暗色微粒包体相应的基性岩浆。长石的复杂结构记录了雁荡山火山-侵入杂岩形成过程中的岩浆混合作用和岩浆演化过程。岩浆混合之后的火山喷发活动,造成岩浆房的压力突然减小,温压条件达到钾长石结晶的区域,在石英正长斑岩的斑晶斜长石和暗色包体中的斑晶与基质斜长石外均形成钾长石膜,构成反环斑结构。     

系外行星大气研究现状

探测系外行星的最终目的是为了寻找系外生命和宣居行星,而系外行星大气是人们了解行星宜居特性的窗口,所以系外行星大气的研究至关重要。近10年来,系外行星大气的理论研究和观测都发展迅速。受观测技术限制,目前观测到大气的系外行星主要是用凌星法探测到的热木星和超级地球,还有用直接成像法探测到的离主星较远的年轻气态巨行星。力求在系外行星大气领域飞速发展之际,对该领域研究现状做简明介绍。首先介绍系外行星大气的观测方法,随后介绍热木星和超级地球的大气概况和研究现状,最后对系外行星大气探测的有关项目进行简要介绍,以展示未来系外行星大气研究的前景。     

样本输入方式对极端学习机预报日长变化的影响

针对极端学习机(Extreme Learning Machine,ELM)用于日长(Length-Of-Day,LOD)变化预报过程中,样本输入方式对预报结果的影响进行了研究。采用跨度、连续和迭代3种样本输入方式对日长变化进行预报。结果表明,不同的样本输入方式对预报结果有很大影响,样本按跨度输入的预报精度最低;样本采用连续输入方式在短期和中长期预报中预报精度较高,但计算速度较慢,较适合中长期预报;样本按迭代输入方式的短期预报精度稍优于连续输入方式,而中长期预报精度则不如连续输入方式,但具有较高的预报效率。这对于日长变化的实时快速预报有着较高的现实意义。     

Machine Learning-based Weather Support for the 2022 Winter Olympics

正1.A key support for the 2022 Winter Olympics The XXIV Olympic Winter Games are scheduled to take place from 4 to 22 February 2022, followed by the Paralympic Games from 4 to 13 March, in Beijing and towns in the neighboring Hebei Province, China. Weather plays an extremely important role in the outcome of the games (Chen et al., 2018). It can not only cause a difference between a medal or not, but affect the safety of athletes. Success of the Winter Olympics will greatly depend on weather conditions at the outdoor competition venues, dealing with many weather elements including the snow surface temperature, apparent tem-     

基于FY-3C微波辐射成像仪海表温度产品的偏差订正方法研究

提出一种针对FY-3C搭载的微波辐射成像仪（MWRI）海表温度产品的分段回归偏差订正方法,该方法通过引进气候态海表温度数据,建立与关联实测海表温度相匹配的回归模型,并通过对模型中关联变量的误差分析,选择最优样本进行分段回归,以实现对海表温度数据的重新估计。通过对MWRI海表温度数据的偏差订正试验表明,采用分段回归方法获得的订正结果无论在误差指标的空间分布还是时间序列上,都要明显优于采用传统概率密度函数偏差订正方法的结果。其中,采用概率密度函数方法订正后的海表温度产品误差标准差和均方根误差从订正前的0.9—1.0℃,减小到0.8℃左右,而采用分段回归方法获得相应的订正误差仅为0.6℃左右,订正效果有明显改善。      

Hiatus of wetland methane emissions associated with recent La Niña episodes in the Asian monsoon region

The variability of methane emissions from wetlands in the tropics and northern temperate regions can explain more than 70% of the interannual variation in global wetland methane emissions, which are largely driven by climate variability. We use climate reanalysis, remote sensing wetland area dataset and simulations from 11 land models contributing to Global Methane Budget to investigate the interannual variation and anomalies of wetland methane emissions in the Asian Monsoon region. Methane emissions in this region steadily increased over 2000–2012. However, abnormally low methane emissions were found in equatorial fully humid (Af), warm temperate winter dry (Cw), and warm temperate fully humid (Cf) Asian Monsoon climate sub-regions in 2008, 2009 and 2011, respectively. These spatially-shifting low emissions occurred simultaneously with observed wetland area shrinkage due to abnormally low precipitation. Interannual variability of wetland methane emissions in Asian Monsoon region are primarily driven by South Asian Monsoon system. However, the abnormally low emissions are related to strong La Niña events, and its accompanying effect of weakened East Asian Monsoon system and eastward Western Pacific subtropical high, which drives the shifting pattern of rainfall, and thus the spatial pattern of methane emission anomalies.     

Decreasing precipitation occurs in daily extreme precipitation intervals across China in observations and model simulations

Extreme precipitation response to increasing temperature includes not only changes of frequency and intensity, but also changes of extreme precipitation interval (EPIV) and the precipitation during the neighboring daily extreme precipitations interval (EPIP). These changes have not been fully evaluated yet in observations or climate model simulations although they are very useful to understand variations of extreme precipitation. We used daily precipitation data from 669 meteorological stations during the past five decades across China and projections of 19 general circulation models from CMIP5 under the RCP4.5 and RCP8.5 scenarios to investigate variations of EPIV and EPIP. We found the national average annual EPIV increased across China during the last five decades, while annual EPIP significantly decreased. The decreases mainly occurred in southwest China, east China, and southeast China. At national and regional scales, the average annual EPIV and EPIP showed greater decreases under the RCP8.5 scenario than those under the RCP4.5 scenario from 2006 to 2100. Annual EPIP showed a stronger correlation with extreme precipitation intensity than EPIV. The national average annual EPIP had a significant positive correlation with the Western Pacific Subtropical High Area Index. The abnormal geopotential heights over western Mongolia and the western Pacific at 500 hpa as well as the abnormal SSTs in Japan Sea and the western of Pacific in rainy seasons would result in abnormal annual EPIVs and EPIPs in China. This study may provide references for flooding prediction, water resources management, and disaster prevention and mitigation.     

合成孔径雷达森林资源监测技术研究综述

合成孔径雷达（Synthetic Aperture Radar,SAR）技术凭借其全天时、全天候的成像能力以及对森林垂直结构信息敏感的特点,在森林资源监测中具有独特的优势,已成为当前森林资源遥感调查技术的研究热点.本文首先介绍了SAR森林资源监测技术的发展背景、发展轨迹和相关知识;然后,重点阐述了极化SAR、干涉SAR、极化干涉SAR和层析SAR在林地覆盖类型分类、变化检测以及森林参数定量化估测应用中的技术方法;最后,就SAR在森林资源监测研究和应用中存在的问题与发展趋势进行了总结与展望.     

信息网络管理及安全防范

1信息网络存在的问题(1)信息网络硬件配置的安全策略不够全面。一般都能够部署边界防火墙和配备网络病毒查杀软件等基础设施,但缺乏有针对性及病毒库更新及时的防护软件。在内外网分离的工作环境中,内网也只重视边界防护,一旦突破内网,安全无法保障。部分业务和网络节点还存在缺陷,对网络技术的恶意代码检测手段及安全事件的事中分析、事后溯源能力不足,因此存在被网络攻击的风险。     

起伏地形下河南省太阳散射辐射的分布式模拟

利用河南省及周边145个气象站1961－2000年常规气象观测资料和河南省1:25万DEM数据,充分考虑起伏地形下太阳散射辐射的天空因素与地面因素后,基于分布式开阔度模型和天文辐射模型,实现了起伏地形下河南省太阳散射辐射的分布式模拟.计算了100m×100 m分辨率下河南省1－12月气候平均太阳散射辐射及多年平均年散射辐射总量的空间分布.结果表明:在充分考虑经验系数的时空分布特征后,模拟精度有了进一步提高.与郑州站的观测资料对比验证表明,模拟精度较高,年平均绝对误差为3.06 MJ·m-2,年平均相对误差为1.67％;局地地形对太阳散射辐射的影响比较明显;通过个例年验证对模型性能和模拟结果进行考察,年平均相对误差不足11％.综上表明模型的时空模拟性能良好.