Karyological analysis of the sea cicada Blepharipoda liberate Shen from the Rizhao intertidal zone,China

Journal of Oceanology and Limnology - Blepharipoda liberate Shen is a commercially valuable seafood species that has important ecological significance in Shandong Province, China. Although B....     

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Storm surges pose significant danger and havoc to the coastal residents' safety, property, and lives, particularly at offshore locations with shallow water levels. Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans. In addition to experienced predictions and numerical models, artificial intelligence (AI) techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations. Convolutional neural network (CNN) and long short-term memory (LSTM) are two of the most important models among AI techniques. However, they have been scarcely utilised for surge level (SL) forecasting, and combinations of the two models are even rarer. This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information. The architectures of the CNN, LSTM, and two sequential techniques of combining the models (LSTM–CNN and CNN–LSTM) were constructed via a trial-and-error approach and knowledge obtained from previous studies. As a case study, 11 a of hourly observed SL and wind data of the Xiuying Station, Hainan Province, China, were organised as inputs for training to verify the feasibility and superiority of the proposed models. The results show that CNN and LSTM had evident advantages over support vector regression (SVR) and multilayer perceptron (MLP), and the combined models outperformed the individual models (CNN and LSTM), mostly by 4％–6％. However, on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges, the accuracy was found to improve by over 10％ at all forecasting steps.     

加州鲈鱼的繁殖习性,早期胚胎发育以及孵化与水温的关系

作者对加州鲈鱼繁殖习性及早期胚胎发育进行了阐述,对受精卵孵化与水温的关系进行了研究。结果表明:加州鲈鱼2龄即达性成熟,3—6月繁殖产卵,水温20℃—24℃时达产卵盛期;雄鱼具有筑巢护卵及护苗等生殖习性;水温17.4℃—19.5℃,平均18.3℃时,受精卵孵化时数为74小时25分钟;在实验温度范围内,孵化时数与水温负相关,而与有效积温正相关。有效积温与平均水温负相关。     

西藏中部的发震构造

西藏中部的发震构造是晚第四纪以来强烈活动的北东、北西和近南北向的活动断层及受其控制的地堑半地堑型盆地和拉分盆地，东西向缝合线和断层晚第四纪以来的运动性不明显，对地震活动没有明显的控制作用，活动断层的规模和晚第四纪以来的运动性质对地震的震级和数量有明显的的控制作用。８级地震的发生与拉分盆地内部张剪切断层与盆地一侧新活动幅度大的次级走滑断层的相互作用密切相关。     

波动性生态脆弱带的特征

波动性生态脆弱带是生态脆弱带的基本类型之一,跃变性是波动性的实质,降水的高变率与人类活动的保守性、稳定性的尖锐矛盾是形成脆弱的主要原因。牧草、粮食的放大性是对降水波动性的进一步体现,波动性是对农林牧土地利用结构,经济水平,人口超载状况的综合反映,是度量脆弱度的实用指标之一。     

基于探空资料的江西典型冻雨天气过程垂直结构分析

利用探空资料和地面观测资料,对1959—2008年发生在江西的典型冻雨天气过程的大气垂直结构进行了分析。结果表明:冻雨的形成与大气饱和层、冰晶层、暖层、逆温层、冷层及特征物理量等因素的关系密切。对流层中下层的大气呈饱和或准饱和的逆温状态,饱和层顶位于暖层之上是冻雨发生的主要层结特征。冰晶层底部的平均高度为675hPa,平均厚度为2309m;暖层底部的平均高度为834hPa,平均温度为4.1℃,平均厚度为1765m;逆温层的平均温差为7.5℃;冷层的平均厚度为1668m,平均最低温度为-3.9℃;平均地面最低温度为-1.2℃;冻雨发生时大气整层比湿积分指数须增大为1000以上,干暖盖强度指数须降到-5℃以下。     

The Belt and Road: Geographical pattern and regional risks

Journal of Geographical Sciences - Building the Belt and Road is initiatives of China to promote win-win international cooperation in the new era, aiming at green, health, intellect and peace and...     

西宁曹家堡机场雷暴统计特征分析

基于2006—2015年西宁曹家堡机场的雷暴观测资料,从雷暴日（次）数、持续时间、初现方位、伴随天气现象和雷暴初终日特征方面对该高原机场的雷暴特征进行统计分析。结果表明,曹家堡机场年平均雷暴日数为34.7 d,主要发生在5—9月,夏季是该机场雷暴的集中爆发期;雷暴具有明显的日变化特征,集中发生在午后至夜间（16—22时）,持续时间在2 h以内;雷暴初现方位以偏西、偏北为主,偏西、偏北的对流云团发展是该机场飞行保障的关注重点;雷暴伴随天气现象以干雷暴和小阵雨为主,而少数伴有中、大阵雨的强雷暴主要发生在夏季;雷暴初终日平均间隔为173.2 d,并且初雷能充分体现该机场的典型雷暴特征。     

基于DDS技术的航空重力仪电容测微正弦激励源设计

介绍基于直接数字合成技术(DDS)研制的正弦激励源系统工作原理,运用matlab-simulink仿真模块仿真正弦激励源数据,结果表明,幅值稳定性可达到10-6~10-7,频率稳定性达到10-4,该系统能解决以往正弦电路中幅值稳定度和高频时频率稳定度差的问题,提高航空重力仪的观测精度。     

泉州湾海域海浪谱

本文根据福建泉州湾大量实测海浪谱资料,分析了该海域海浪内部结构特征,比较和检验了国内外常用的Ｐ－Ｍ谱、Ｊｏｎｓｗａｐ谱、规范谱及国内沿岸海区的一些经验谱,结果取了与本海区实测海浪个例谱结构接近程度最好的Ｊｏｎｓｗａｐ谱拟合泉州湾海浪谱模式。