利用微信企业号开展川岛航线气象服务

通过新浪云服务器SAE(Sina App Engine)及PHP编程,采用C/S架构,以"江门市气象公共服务中心"微信企业号为例,介绍如何利用微信企业号开展川岛航线专业气象服务,重点介绍川岛航线小时极大风超阈值自动推送原理,实现移动端专业气象信息发布的自动化和智能化,提高气象实况信息的监测和发布效率,从而满足专业用户实际需求,达到气象防灾减灾目的,也减轻业务工作压力,使专业气象服务增值。     

基于微信平台的气象预警信息发布系统

以微信公众服务平台群发功能为基础,建立气象预警信息发布微信虚拟服务器,结合潜江市气象局已有的气象信息资料,组建潜江气象预警信息发布系统。通过服务器向用户群发布文本、声音、图像、视频、图文等气象预警信息,实现图文并茂信息推送、信息查询、用户管理、人机交互等功能。系统采用多进程并发命令,快速发布预警信息;设立潜江气象小助手微信用户小号,发布消息预警声音提示。系统预警发布迅速,信息畅通,内容丰富,提升了潜江市气象防灾减灾应对能力。     

浅谈利用数据库技术实现"12121"自动外呼系统的管理

"12121"自动外呼系统的使用对河池市气象局及时、广泛传播气象预警信息起到了重要的作用,而数据库技术在"12121"自动外呼系统中的应用又进一步实现了预警信息分区域发布的功能和实现对用户的有效管理     

基于微信模板消息的天气预警智能推送系统建设

天气预警智能推送系统的建设是以微信模板消息功能特点为基础,同时结合天气预警推送需要,实现了基于位置的天气预警信息点对点发布。根据微信提供的模板消息,该系统可针对预警信息、突发应急信息、强灾害性天气影响等采用不同的模板来实现信息的智能推送。系统设计后台采用多线程处理技术,提高预警推送效率,确保预警信息在10 min之内到达用户端,并且利用用户ID绑定等方式实现用户对预警信息的分区自主定制。基于微信模板消息的天气预警智能推送系统是精细化预警发布方式的重要探索,很好地提高天气预警信息传播覆盖面和传播效率。     

气象灾害监测预警信息系统开发与应用

根据气象防灾减灾、气象预警信息发布的需要,结合计算机数据库系统、自动气象站(山洪灾害)布点加密观测数据、移动通信技术研制开发成智能交互式的气象灾害监测预警信息系统,做到自动报警预警并能随时随地通过手机即时交互反馈信息。从而提高气象、国土、水务、应急等部门预警及防灾减灾的应用水平及服务地方政府和专业气象服务用户的能力,系统使用C/S构架,以SQL server及ACCESS为后台数据库,以Delphi为前台开发平台,结合SQL结构化查询语言,以API数据库接口方式进行数据信息的推送。     

停课铃桌面插件系统的设计与实现

为了在PC端建立一个可实现"主动推送"停课消息和预警信息的服务渠道,开发停课铃桌面插件系统。停课铃桌面插件系统采用模块化低耦合架构设计,其推送网关运用Node.js平台,与突发公共事件预警信息发布系统建立TCP长连接,实时接收预警、停课消息,并向客户端推送,使其成为气象部门一个新的应急气象高效服务渠道。停课铃桌面插件还拥有天气实况、逐天预报、逐时预报、空气质量等多个功能模块,是公众安排工作生活事宜的参考"利器"。     

基于“互联网+”的气象微信服务技术探析

利用GAP信息交换、自动快速推送、记忆用户定位信息、微网页融入微信服务等技术研究开发了基于"互联网+"的"漳州气象"微信,该微信在网络安全的基础上能够针对不同用户自动快速推送相应的预警信息。     

基于Android的气象信息发布系统的实现

介绍了基于Android的气象信息发布系统,该系统实现的功能是让已连接Internet的Android系统手机、专业液晶触控平板电视在任何时间、任何地点,自动实时获取气象部门最新发布的重大灾害性天气预警信号、常规天气预报及气象科普知识等内容,从而提高预报信息的覆盖面。目前系统用户主要包括乡镇、医院、社区、消防部门、企业和农民专业合作社。与传统的气象信息发布平台相比,该系统具有易操控、互动效果好等优势。     

基于微信的气象灾害预警信息精准智能推送技术的实现

本文提出了基于"江西预警发布"微信服务号的预警信息精准智能推送平台的设计思路和总体框架,搭建了由运行支撑层、数据层、服务层和业务应用层组成的业务平台,实现了基于位置的网格化临近预报信息的实时发布和预警信息的靶向发布,预警服务模式从"单向推送"向"双向互动"转变。研究应用了多项关键技术支持平台的智能化,包括基于位置服务(LBS)的实时预警、临近雨情、预警定制、定向群发和后台管理等平台核心功能技术。平台操作简单,使用便捷,为用户实时实地提供量身定制的预警服务。     

一体化微信气象为农服务平台设计

一体化微信气象为农服务平台基于微信公众服务号"江西微农"进行二次开发,集天气预报、气象灾害预警、天气雷达、卫星云图等天气服务,病虫情报、农事指南、农业政策、农技推广等农业服务以及灾情互动、农情反馈、提问咨询等互动交流功能为一体,为用户提供了功能齐全、信息丰富、专业权威的气象、农业一体化综合服务。服务平台开发包括了"江西微农"后台管理系统,可以为省、市、县各部门管理员进行用户管理、消息推送、产品维护以及与用户交流提供方便易用的管理工具,显著减少业务维护工作量,实现省、市、县一体化管理,保障各项服务高效稳定运行。     

Could the Recent Taal Volcano Eruption Trigger an El Ni?o and Lead to Eurasian Warming?

正The Taal Volcano in Luzon is one of the most active and dangerous volcanoes of the Philippines. A recent eruption occurred on 12 January 2020(Fig. 1a), and this volcano is still active with the occurrence of volcanic earthquakes. The eruption has become a deep concern worldwide, not only for its damage on local society, but also for potential hazardous consequences on the Earth's climate and environment.     

Analysis of Atmospheric Boundary Layer Height Characteristics over the Arctic Ocean Using the Aircraft and GPS Soundings

正ERRATUM to: Atmospheric and Oceanic Science Letters, 4(2011), 124-130 On page 126 of the printed edition (Issue 2, Volume 4), Fig. 2 was a wrong figure because the contact author made mistake giving the wrong one. The corrected edition has been updated on our website. The editorial office is sincerely sorry for any     

Index to Vol.31

正AN Junling;see LI Ying et al.;(5),1221—1232AN Junling;see QU Yu et al.;(4),787-800AN Junling;see WANG Feng et al.;(6),1331-1342Ania POLOMSKA-HARLICK;see Jieshun ZHU et al.;(4),743-754Baek-Min KIM;see Seong-Joong KIM et al.;(4),863-878BAI Tao;see LI Gang et al.;(1),66-84BAO Qing;see YANG Jing et al.;(5),1147—1156BEI Naifang;     

Information for Contributors

正Journal of Meteorological Research is an international academic journal in atmospheric sciences edited and published by Acta Meteorologica Sinica Press,sponsored by the Chinese Meteorological Society.It has been acting as a bridge of academic exchange between Chinese and foreign meteorologists and aiming at introduction of the current advancements in atmospheric sciences in China.The journal columns include Articles.Note and Correspondence,and research letters.Contributions from all over the world are welcome.     

前寒武纪气候演化中的三个重要科学问题

自地球形成至寒武纪将近40亿年（距今46亿～5.4亿年,通常称为前寒武纪）的气候演变是一个具有特殊难度和挑战性的研究领域,同时也是基础和前沿的研究领域。文章选择了前寒武纪气候演化中的三个重要科学问题进行综述：大气演化、两次全球性的冰川期以及暗弱太阳问题。关于大气演化,本文首先描述了大气成分的演化历史,然后简述了影响大气成分演化的三个基本过程：大气逃逸、两次大气氧含量突然增加、碳酸盐-硅酸盐循环及其对气候系统的负反馈作用。两次全球性的冰川期分别发生在古元古代（距今24亿～21亿年）和新元古代（距今8亿～5.8亿年）,文章简述了其成因以及相关的气候模拟结果。暗弱太阳问题是地球历史气候演化的一个经典问题,论文简要地综述了一些最新的研究成果和观点。     

淮河流域水文极值预测模型研究

为探索气候变化影响下水文极值的非平稳性和预测方法,建立了水文极值非平稳广义极值（GEV）分布的统计预测模型。利用1952-2010年淮河上游流域累计面雨量和流量年最大值资料、同期500 hPa环流特征量资料以及17个CMIP5模式对环流特征量的模拟结果,筛选出对水文极值影响显著的年平均北半球极涡强度指数作为GEV分布参数的预测因子。分析了在RCP2.6、RCP4.5和RCP8.5情景下2006-2050年淮河上游流域水文极值对气候变化的响应。结果表明,10年以下与10年以上重现期的水文极值在非平稳过程中呈现前者下降而后者上升的相反变化趋势;多模型预测的集合平均在未来情景中均呈现上升趋势,情景排放量越大增幅越大,重现期越长增幅也越大。与极值的常态相比,极值的极端态更易受气候变化影响。     

ANALYSIS OF “BIMODAL PATTERN” STORM ACTIVITY CHARACTERISTICS OF THE BAY OF BENGAL

Storms that occur at the Bay of Bengal (BoB) are of a bimodal pattern, which is different from that of the other sea areas. By using the NCEP, SST and JTWC data, the causes of the bimodal pattern storm activity of the BoB are diagnosed and analyzed in this paper. The result shows that the seasonal variation of general atmosphere circulation in East Asia has a regulating and controlling impact on the BoB storm activity, and the “bimodal period” of the storm activity corresponds exactly to the seasonal conversion period of atmospheric circulation. The minor wind speed of shear spring and autumn contributed to the storm, which was a crucial factor for the generation and occurrence of the “bimodal pattern” storm activity in the BoB. The analysis on sea surface temperature (SST) shows that the SSTs of all the year around in the BoB area meet the conditions required for the generation of tropical cyclones (TCs). However, the SSTs in the central area of the bay are higher than that of the surrounding areas in spring and autumn, which facilitates the occurrence of a “two-peak” storm activity pattern. The genesis potential index (GPI) quantifies and reflects the environmental conditions for the generation of the BoB storms. For GPI, the intense low-level vortex disturbance in the troposphere and high-humidity atmosphere are the sufficient conditions for storms, while large maximum wind velocity of the ground vortex radius and small vertical wind shear are the necessary conditions of storms.     

LOW-FREQUENCY CIRCULATION AND EXTENDED-RANGE FORECAST IN CONNECTION WITH AUTUMN EXTREME HEAVY RAINFALL PROCESS IN HAINAN

Observed daily precipitation data from the National Meteorological Observatory in Hainan province and daily data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-2 dataset from 1981 to 2014 are used to analyze the relationship between Hainan extreme heavy rainfall processes in autumn (referred to as EHRPs) and 10–30 d low-frequency circulation. Based on the key low-frequency signals and the NCEP Climate Forecast System Version 2 (CFSv2) model forecasting products, a dynamical-statistical method is established for the extended-range forecast of EHRPs. The results suggest that EHRPs have a close relationship with the 10–30 d low-frequency oscillation of 850 hPa zonal wind over Hainan Island and to its north, and that they basically occur during the trough phase of the low-frequency oscillation of zonal wind. The latitudinal propagation of the low-frequency wave train in the middle-high latitudes and the meridional propagation of the low-frequency wave train along the coast of East Asia contribute to the ‘north high (cold), south low (warm)’ pattern near Hainan Island, which results in the zonal wind over Hainan Island and to its north reaching its trough, consequently leading to EHRPs. Considering the link between low-frequency circulation and EHRPs, a low-frequency wave train index (LWTI) is defined and adopted to forecast EHRPs by using NCEP CFSv2 forecasting products. EHRPs are predicted to occur during peak phases of LWTI with value larger than 1 for three or more consecutive forecast days. Hindcast experiments for EHRPs in 2015–2016 indicate that EHRPs can be predicted 8–24 d in advance, with an average period of validity of 16.7 d.