基于Windows 2003远程终端服务的气象信息共享平台的开发和应用

Windows 2003 Server提供的远程终端服务功能实现了多用户同时操作,远程终端多用户通过远程终端功能可以同时在远程终端服务上运行程序、保存文件和使用网络资源等,为业务应用提供了便捷.介绍利用Windows Server 2003远程终端(包括远程桌面和终端服务),开发了气象业务中多用户共享MICAPS20、雷达PUP产品及其它气象应用系统的信息服务平台,在实际应用中发挥了很好的应用效果.     

Detecting Arctic snow and ice cover with FY-1D global data

Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.     

CHINA’S FOUR MAJOR METEOROLOGICAL SCIENTIFIC EXPERIMENTS

China lies in East-Asian monsoon region,which is one of the well-known active monsoon zones around the world.Monsoon anomaly results in frequent natural disasters,such as drought,torrential rain and flood.In 1998,joint intensified observations for 4 major meteorological scientific experiments have been carried out over Chinese major monsoon affected areas.A number of valuable data have been obtained and some observational facts have come out after initial analysis.The present paper is to give an introduction to the 4 major meteorological scientific experiments conducted in 1998 in China.including its origin and scientific goals,implementation and planning,equipment and progress,and initial findings from the important observational facts.It aims to provide a comprehensive report on the progress of the above experiments for those who are interested in.     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

广西山地风能资源开发利用可行性分析——以大容山为例

利用大容山自动观测站2004年8月～2006年7月气象观测资料和容县气象站1958～2006年7月测风资料,对大容山的风能资源各参数进行了详细计算和分析,并结合预选风电场的地形地貌、交通运输、联网条件、环境保护等状况对大容山风能资源开发利用进行了可行性分析评价.结果表明：大容山年平均风速为7.2m/s,年平均风功率密度为390.8W/m^2,其风能资源丰富,且交通运输、联网、工程地质等条件较好,可选择在坡度较小的山头或山腰建设风电场,预选风电场可布置750kW的风机45台,总装机容量约为34MW.     

现行业务湿度传感器测量不确定度研究

依据JJF1059.1-2012《测量不确定度评定与表示》,针对自动气象站各要素传感器的测量误差会随时间、温度等环境因素的变化而发生漂移.为确保观测数据的准确、可靠,以CP340恒温恒湿箱为湿度发生环境、采用英国michell公司的Optidew Vision精密露点仪为标准器,介绍了利用HMP155A温湿度传感器进行湿度测量不确定度评定的一般方法和详细步骤.最后给出一个评定实例,在温度为22℃和40℃,相对湿度为30％ RH～75％RH时,通过对测量不确定度的各分量进行分析和计算,得到HMP155A温湿度传感器测量结果的扩展不确定度分别为1.50％和1.46％.     

MPI环境下基于边界校正方法的海洋模型的并行化

基于区域分解技术,设计了一种针对海洋模型的边界校正方法,该方法不需要对原有海洋模型进行较大改动,只需要利用消息传递接口(message passing interface,MPI)编写子区域间的数据传输模块,即可实现原有海洋模型的并行化。相对于重新开发并行系统,该方法能够较快把原有模型的串行解决方案转化为并行解决方案,并且并行化后的海洋模型能够获得较高的加速比。     

渤海西部2次赤潮过程的气象因子对比分析

利用加密自动站资料、NCEP/NCAR 1°×1°再分析资料、海表温度(SST)资料和常规观测资料等气象资料,对2009年5月31日—6月13日和2012年5月18—28日发生在渤海西部海域的2场相似气象因子影响下的典型高影响赤潮过程进行了对比分析。初步结果表明:(1)2场高影响赤潮过程都发生在春末夏初的赤潮高发期,持续时间在10天以上;(2)赤潮爆发前期,伴随有暖湿气流影响形成适度降水,渤海西部的周平均海表温度(SST)升温明显,且SST≥16.2℃,赤潮发生海域为SST的暖脊控制,对赤潮过程的潜势预报有明显的指示意义;(3)850 h Pa以下层次的温度迅速上升,近地层西南暖湿气流的加强和地面6 m/s以下偏南、东南气流在渤海西部海域的汇合,利于渤海西部海洋浮游生物积聚和突发性繁殖,是赤潮形成的重要气象因子特征。     

西藏气象指数通用计算系统

利用人机交互方式定义气象指数计算公式,设计了西藏地区常用气象指数计算系统。该系统在统一的操作界面上定义和读取输入气象数据,包括数值预报、实况数据、预报产品等数据及站号、经度、纬度和时间信息,将相应的数据以浮点数代入公式,并支持加、减、乘、除、乘方等数学运算符,三角对数、绝对值等数学函数,且、或、否等逻辑函数,≥、＞、≤、＜、=等判断运算符及自定义的分段函数,能够完成多项气象指数的计算,数据均以通用的MICPAS格式交互存储,在统一的平台上管理气象指数及产品制作,无需预报和服务人员编程即可实现新的气象指数。     

丽水市旅游气候舒适度分析

采用人体舒适度气象指数（BCMI）、寒冷指数（CI）、温湿指数（THI）和度假气候指数（HCI）等4个综合性的气候指标,对丽水市的旅游气候舒适度进行了分析评价,并着重对比分析了BCMI和HCI对旅游气候适宜性的表征能力。结果表明：丽水市全年有8个月BCMI处在4～6级之间,属一类气候适宜区;冬季CI值较低,无严寒现象,夏季低海拔地区THI值偏高,但海拔600 m以上地区（约占市域面积6成）THI值较低,适宜“避暑纳凉”;HCI比传统的BCMI表现的更客观、更全面,它不仅考虑了气候的热舒适性影响,还考虑了降水和云量对旅游出行及观光的影响,且就时间尺度而言,HCI更适合旅游舒适度逐日预报的开展。