Developing the Science Product Algorithm Testbed for Chinese Next-Generation Geostationary Meteorological Satellites：Fengyun-4 Series

作     者：Min MIN Chunqiang WU Chuan LI Hui LIU Na XU Xiao WU Lin CHEN Fu WANG Fenglin SUN Danyu QIN Xi WANG Bo LI Zhaojun ZHENG Guangzhen CAO Lixin DONG 

作者机构：Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological Center China Meteorological Administration Beijing 100081 

出 版 物：《Journal of Meteorological Research》 (气象学报（英文版）)

年 卷 期：2017年第31卷第4期

页      面：708-719页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：National Natural Science Foundation(41405035,41571348,and 41405038) China Meteorological Administration Special Public Welfare Research Fund(GYHY201406011 and GYHY201506074) 

主　　题：geostationary meteorological satellite FY-4 algorithm testbed cloud properties 

摘      要：Fengyun-4A（FY-4A）, the first of the Chinese next-generation geostationary meteorological satellites, launched in2016, offers several advances over the FY-2： more spectral bands, faster imaging, and infrared hyperspectral measurements. To support the major objective of developing the prototypes of FY-4 science algorithms, two science product algorithm testbeds for imagers and sounders have been developed by the scientists in the FY-4 Algorithm Working Group（AWG）. Both testbeds, written in FORTRAN and C programming languages for Linux or UNIX systems, have been tested successfully by using Intel/g compilers. Some important FY-4 science products, including cloud mask, cloud properties, and temperature profiles, have been retrieved successfully through using a proxy imager, Himawari-8/Advanced Himawari Imager（AHI）, and sounder data, obtained from the Atmospheric Infra Red Sounder, thus demonstrating their robustness. In addition, in early 2016, the FY-4 AWG was developed based on the imager testbed—a near real-time processing system for Himawari-8/AHI data for use by Chinese weather ***, robust and flexible science product algorithm testbeds have provided essential and productive tools for popularizing FY-4 data and developing substantial improvements in FY-4 products.