The application of FY-2 operational products which produced by the National Satellite Meteorological Center (NSMC) and distributed by CMACast,especial for good quantitative products,is needed to be strengthened.In this study,we successfully decoded and visualized the FY-2G AMV and OLR products automatically using Python,and compared the results with SWAP software.The results show that the images produced by Python and SWAP are quite the same,but wind bars are more evenly distributed for the AMV discrete field data produced by Python,and the products produced by the Gouraud Shading method supported by Python is more smooth than those by the Flat Shading method used in SWAP for the OLR grid field data.According to the automatic and aesthetic advantages of Python visualized reality,the method has great potential in the operational meteorological application.%利用Python语言实现了每日对CMACast下发的最新风云二号G星(FY-2G)云导风(AMV)和射出长波辐射(OLR)两个定量业务产品的实时自动解码和可视化出图,并与国家卫星气象中心下发的卫星天气应用平台(SWAP)的显示进行了对比.结果表明:两者显示一致.在显示所有AMV文件风矢量数据的前提下,本文可视化产品较目前版本SWAP的显示更为疏密均匀,没有出现重叠现象;Python出图自带的高洛德(Gouraud)明暗处理渲染方式则在对OLR格点产品可视化显示中提供了一种更为平滑,细腻的方法.利用本研究方法对风云二号卫星定量产品进行可视化具备自动化和视觉美观的特点,值得在基层气象部门业务中推广.
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