FY-4A卫星数据可视化及应用
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摘要
2016年12月11日我国发射了FY-4A新一代静止气象卫星,其高时间和空间分辨率能够加强航空气象中的监测应用,为了更好地应用FY-4A卫星的各类观测产品,本文利用Python语言实现对FY-4A卫星AGRI观测仪器所探测的2km、4km分辨率的全圆盘和中国区域可见光、红外图像资料及7.8km分辨率闪电监测资料进行解码并可视化显示出图,将绘制的卫星图像与国家卫星气象中心网站对外开放的卫星云图进行对比。结果表明:两者显示较为一致,可以用于航空气象业务。Python语言语法简练,对于卫星HDF和NC格式数据读取速度快,Python的Numpy工具包基于矩阵的运算能快速处理卫星观测数据,Basemap库中多种投影方式可供业务应用,值得推广。
On 11 December 2016,China launched FY-4 A,a new-generation geostationary meteorological satellite,whose high temporal and spatial resolution can enhance the monitoring applications in aeronautical meteorology.In order to better apply the various observation products of FY-4 Asatellite,this paper uses the Python language to realize decoding and visualizing of the 2-km and 4-km resolution full discs,the visible and infrared images over China,and 7.8-km resolution lightning monitoring data detected by the AGRI observation instrument of FY-4 Asatellite,and compares the resulted satellite images with those from the website of the National Satellite Meteorological Center that are open to public.The results show that the two sorts of images are consistent and can be used in aeronautical meteorological services.The Python language syntax is concise and has fast reading speed for satellite HDF and NC format data.Python's Numpy toolkit based on matrix operation can quickly process satellite observation data.The various projection methods in the Basemap library are available for operational application,which is worthy of promotion.
On 11 December 2016,China launched FY-4 A,a new-generation geostationary meteorological satellite,whose high temporal and spatial resolution can enhance the monitoring applications in aeronautical meteorology.In order to better apply the various observation products of FY-4 Asatellite,this paper uses the Python language to realize decoding and visualizing of the 2-km and 4-km resolution full discs,the visible and infrared images over China,and 7.8-km resolution lightning monitoring data detected by the AGRI observation instrument of FY-4 Asatellite,and compares the resulted satellite images with those from the website of the National Satellite Meteorological Center that are open to public.The results show that the two sorts of images are consistent and can be used in aeronautical meteorological services.The Python language syntax is concise and has fast reading speed for satellite HDF and NC format data.Python's Numpy toolkit based on matrix operation can quickly process satellite observation data.The various projection methods in the Basemap library are available for operational application,which is worthy of promotion.
引文
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[2]张鹏,郭强,陈博洋,等.我国风云四号气象卫星与日本Himawari-8/9卫星比较分析[J].气象科技进展,2016(6):72-75.
[3]邓莲堂.GrADS绘图汉字显示的技术研究与实现[J].气象,2010,36(11):115-119.
[4]王继竹,王咏春,李春虎.NCL在气象数据图形化中的应用[J].山东气象,2007,27(2):33-36.
[5]何全军,张月维,曹静,等.基于IDL的MODIS1B数据SST反演[J].热带气象学报,2009,25(2):205-208.
[6]陈林,牛生杰,仲凌志.Matlab对基于HDF格式的MODIS1B数据的提取方式与实现[J].气象科学,2006,26(6):676-681.
[7]高大伟,郁珍艳,姚益平,等.Python在FY-2G定量业务产品自动可视化中的应用[J].气象科技,2018,46(1):43-50.
[8]王清平.基于python的气象自动站数据解码与显示[C]∥第31届中国气象学会年会---S13气象通信与信息技术应用实践与新技术探索.北京:中国气象学会,2014.
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[10]国家卫星气象中心.FY-04A成像仪上行列号和经纬度的互相转换方法[EB/OL].[2018-06-17].http:∥satellite.nsmc.org.cn/PortalSite/StaticContent/DocumentDownload.aspx?TypeID=10.