Spatio-temporal distribution of Konosirus punctatus spawning and nursing ground in the South Yellow Sea

In recent years, Konosirus punctatus has accounted for a large portion in catch composition and become important economic species in the South Yellow Sea. However, the distribution of K. punctatus early life stages is still poorly understood. In this study, generalized additive models with Tweedie distribution were used to analyze the relationships between K. punctatus ichthyoplankton and environmental factors(longitude and latitude, sea surface temperature(SST), sea surface salinity(SSS) and depth), and predict distribution K. punctatus spawning ground and nursing ground, based on samplings collected in 6 months during 2014–2017. The results showed that K. punctatus' spawning ground were mainly distributed in central and north study area(from 33.0°N to 37.0°N).By comparison, the nursing ground shifted southward, which were approximately located along central and south coast of study area(from 31.7°N to 35.5°N). The optimal models identified that suitable SST, SSS and depth for eggs were 19–26°C, 25–30 and 9–23 m, respectively. The suitable SSS for larvae were 29–31. The K. punctatus spawning habit might have changed in the past decades, which was a response to increasing SST and fishing pressure. That needs to be proved in further study. The study provides references of conservation and exploitation for K. punctatus.     

黔西南州一次分裂型超级单体风暴环境条件和回波结构分析

应用兴义多普勒雷达资料和相关实况资料,分析了2009年4月14日傍晚发生在黔西南州安龙、兴义和兴仁的一次强雹暴天气的环境条件和雷达回波结构,详细分析了强雹暴发展过程的回波结构特征,以及在超级单体中观测到的中反气旋流场结构.雹暴发生在地面加热强烈,低层热低压发展,中层有小槽发展东移,高空有急流的背景下.小槽触发的对流云团...     

边界层风廓线雷达资料在北京夏季强降水天气分析中的应用

利用北京城区及周围3个站的Airda 3000边界层风廓线雷达提供的风廓线资料,详细分析了北京2005年8月3日的一次强降水天气过程.分析表明,降水前十几小时出现双层低空急流,急流层内结构复杂,呈现多中心结构.风廓线观测揭示,南高空槽和弱冷空气共同诱发产生的切变线低涡是产生此次暴雨天气的主要中尺度系统,暴雨系统有很复杂的垂直结构.强降水开始前数小时(夜间)城区地面风场辐合,在临近降水和降水开始时辐合(或切变)层向上发展,这一过程有利于降水的发展.     

近30年西藏地区雷暴变化特征

利用西藏地区38个观测站点1979-2008年雷暴资料,采用旋转EOF分析、合成分析、小波分析及趋势分析等统计方法对西藏地区雷暴的时空变化规律进行了研究.结果表明:两藏地区以午后短时热力性雷暴为主,雷暴平均总次数存在准15年的周期变化,年雷暴平均总次数呈逐年减少趋势,2000年以后这种趋势更为明显.雷暴持续期变短,但也...     

山东省春秋季暴雨天气的环流特征和形成机制初探

对山东省春秋季暴雨的气候特征和影响系统进行了分析, 制作了春秋季暴雨的平均环流形势图。分析了2003年春秋季两次大范围暴雨的环流特征和影响系统及暴雨期间大气的热力特征和水汽输送特征, 应用k-螺旋度和倾斜涡度发展理论, 分析了暴雨的形成机制。结果表明:4月暴雨均受气旋影响, 10月暴雨以冷锋影响居多。2003年4月17—18日为气旋暴雨, 10月10—12日为切变线冷锋暴雨。两次暴雨前都有低空偏南风急流向暴雨区输送水汽, 大气强烈增温增湿, 对流不稳定度增大, 湿斜压性增强。强冷锋南下触发对流不稳定能量释放, 产生暴雨。暴雨期间低层正k-螺旋度猛烈发展。暴雨前期中低层MPV1 < 0且MPV2 > 0, 冷锋影响期间MPV1 > 0且MPV2 < 0, 都有利于倾斜涡度发展, 增强了上升运动。     

贵州红枫湖沉积物有机质的酶及微生物降解

文章通过DNA、α-葡萄糖苷酶和硫酸盐还原菌等的变化,研究了贵州红枫湖沉积物中有机质的酶及微生物降解.有机质在微生物及其分泌的胞外酶的作用下被降解,在沉积物深度11cm以下被降解到相对较低的含量.DNA的分布表明表层9cm的沉积物深度内微生物的活动较为强烈,是微生物降解有机质的主要位置.α-葡萄糖苷酶在悬浮层含量最高,达0.75μmol/min*g干沉积物,提示有机质中的淀粉和糖原等物质在悬浮层降解较为激烈,被大量分解;随着沉积物深度的增加α-葡萄糖苷酶活性减弱,在有机质降解明显开始变缓的11cm沉积物中,α-葡萄糖苷酶活性已降低到0.17μmol/ming干沉积物.分子生物学的研究表明红枫湖沉积物表层7cm是硫酸盐还原菌的主要分布位置,结合有机质和SO_2-4含量的研究结果,提示红枫湖沉积物中SO_2-4不可能成为有机质氧化的主要电子受体,硫酸盐还原的限制因素也不是有机质供应.     

湘黔汞矿带相关成矿问题的讨论

湘黔汞矿带的含汞建造与生油层系的特征相似,汞矿床的形成和油气的聚集一样具有“生、储、盖”的成矿地质条件。中寒武统敖溪组及下寒武统清虚洞组既是汞矿的容矿层,也是汞矿的矿源层。汞的成矿流体是一种以地下水为主、包括油气在内的混合热液,成矿作用是在低温低压高盐度的还原环境中进行的,属于沉积成岩－地下水热液的成因类型。     

印度大陆板片前缘撕裂与分段俯冲：来自冈底斯新生代火山-岩浆作用证据

青藏高原碰撞造山带不仅呈现南北不均一性,而且显示东西分段性。以横贯高原腹地的NNE向负磁异常带为界,将冈底斯分为三段。在宽约300km的负磁异常带为代表的中段,近SN向的裂谷和正断层系统、重要地震和现代热水活动、古新世林子宗火山岩系和中新世超钾质火山岩系、以及日喀则弧前盆地集中发育,伴有斑岩型Cu-Mo和成因独特的Au-Cu矿化;在85°E以西的西段,主要发育强烈逆冲推覆系、同碰撞期花岗岩和中新世钾质-超钾质火山岩系,伴有造山型Au矿化;而在90°E以东的东段,主要发育走滑断裂系、同碰撞期花岗岩和中新世埃达克质斑岩,伴有斑岩型Cu-Mo矿化。古新世林子宗火山岩的精细定年和地球化学特征揭示,印度大陆板片向北的俯冲-汇聚至少在50Ma前没有表现出明显的时间差异性。然而,中新世钾质-超钾质岩和大规模花岗岩基的时空分布和地球化学特征反映,印度大陆板片前缘可能发生撕裂,并发生分段式差异俯冲,西段(85°E以西)俯冲规模大,距离远,东段(90°E以东)俯冲规模小,可能未跨过雅江缝合带。沿着负磁异常带两侧的边界裂谷带,高SiO_2煌斑岩和念青唐古拉花岗岩基及相伴钾质火山岩的发育,揭示来源于软流圈地幔的岩浆和高热流穿过板片撕裂带并沿耦合上覆的裂谷带上涌,前者侵位和喷发,后者诱发地壳熔融。90°E与85°E之间的俯冲板片可能由于撕裂、断离和破碎,因而导致斜跨高原腹地的大面积通道式负磁异常带。     

宁中白云母二长花岗岩SHRIMP锆石U-Pb年龄及岩石地球化学特征

宁中白云母二长花岗岩出露于拉萨地块中部,应用SHRIMP锆石U-Pb测年,得到锆石壳部加权平均年龄值分别为190±8Ma、193±7Ma和191±10Ma,代表成岩年龄,属早侏罗世侵入岩;核部的继承锆石年龄值大体可分为2部分:300～500Ma为古生代基底锆石;1000～1500Ma为元古宙基底锆石,个别测点得到2160Ma和2356Ma的古老年龄值,反映源岩比较复杂,并提供了拉萨地块存在中元古-古元古代结晶基底的年龄信息。岩石化学分析结果表明:白云母二长花岗岩的SiO_2为73．14%～78．12%、K_2O为3．42%～5．72%,K_2O＞Na_2 O,AL/CNK为1．22～1．42,刚玉标准分子含量为3．18%～4．69%,属典型的强过铝花岗岩。微量元素Rb含量特别高(410×10~(-6)～737×10~(-6)),Sr含量低(9．2×10~(-6)～49．0×10~(-6)),Ba含量低(20．8×10~(-6)～127×10~(-6));稀土总量低,∑REE为38．59×10~(-6)～97．84×10~(-6),铕亏损中等—强烈(δEu=0．22～0．62);同位素比值~(87)Sr/~(86)Sr为1．150900～0．861037,~(143)Nd/~(144)Nd为0．511879～0．511993,反映岩浆主要来自于上部地壳泥质岩石的部分熔融。宁中白云母二长花岗岩属于S型花岗岩,形成于同碰撞构造环境,成岩时代为早侏罗世,应属于冈底斯构造带印支造山旋回晚期碰撞阶段的产物,对它的综合研究有助于进一步认识和研究冈底斯构造带的历史和构造演化过程。     

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.