风云四号A星可见光反射率同化神经网络观测算子  

A neural network-based forward operator for assimilating the FY-4A visible reflectance

作　　者：何明峰 周永波 HE Mingfeng;ZHOU Yongbo(School of Emergency Management,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Atmospheric Physics,Nanjing University of Information Science&Technology,Nanjing 210044,China;Precision Regional Earth Modeling and Information Center(PREMIC),Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学应急管理学院,南京210044 [2]南京信息工程大学大气物理学院,南京210044 [3]南京信息工程大学精细化区域地球模拟与信息中心,南京210044

出　　处：《遥感学报》2024年第12期3261-3270,共10页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金青年基金(编号:42305161);江苏省基础研究计划(自然科学基金)(编号:BK20210665)。

摘　　要：全天空卫星可见光反射率资料蕴含关键的云雨信息,具有重大的同化潜力。传统的可见光反射率资料同化观测算子基于数值方法求解辐射传输方程,计算效率偏低,尚不能满足资料同化的业务应用需求。鉴于此,本研究在Scheck(2021)神经网络观测算子的基础上,优化了神经网络参数与结构,构建了风云四号A星(FY-4A)先进静止轨道辐射成像仪(AGRI)可见光反射率资料同化的快速观测算子NNFO。NNFO的输入参数包括液相和冰相总云水路径(转换到对数空间)、冰相云水路径与总云水路径之比、液滴有效粒子半径、下垫面反照率、太阳天顶角、卫星天顶角、太阳与卫星的相对方位角,输出数据为对应条件下的天顶反射率。结果表明,NNFO的计算效率是传统观测算子RTTOV-DOM的15倍(串行)或6倍(并行),并且NNFO具有与RTTOV-DOM相当的模拟精度。此外,NNFO可以较好地重构FY-4A/AGRI可见光云图,反射率模拟结果的偏差和均方根误差分别为-0.016和0.143。因此,NNFO具有同化应用的潜力。Satellite all-sky visible reflectance contains critical information on cloud and precipitation.Data Assimilation(DA)of these information has great potential to improve the forecasting skills of numerical weather prediction models.Conventional forward operators for DA of visible reflectance employ numerical methods to simulate radiative transferring processes and suffer from a high computational burden.Therefore,conventional forward operators cannot meet the needs of operational DA.The study constructs a fast,accurate forward operator for DA of visible reflectance data provided by the Advanced Geostationary Radiation Imager(AGRI)onboard the Fengyun-4A satellite.The forward operator is comparable with conventional forward operators in terms of accuracy and outperforms the latter in terms of computational efficiency.A feed-forward neural network is utilized to construct the forward operator.The input parameters of the Neural Network-based Forward Operator(NNFO)include the cloud water path converted into logarithmic space,mixing ratio of ice cloud water path and total cloud water path,effective radius of cloud liquid droplets,underlying surface albedo,solar zenith angle,satellite zenith angle,and relative azimuth angle between the sun and the satellite.Top-of-atmosphere reflectance is the output of NNFO.A series of sensitivity studies is performed to determine the optimal(or suboptimal)neural network settings,which include 5 hidden layers,57 nodes in each hidden layer,the Swish activation function for the hidden layers,and batch size of 512.In addition,the neural network is trained with an adaptive learning rate depending on the training epoch and the loss for the validation dataset,which is defined by the Root Mean Square Error(RMSE).NNFO is compared with RTTOV-DOM,a typical forward operator based on the discrete ordinate method for simulating radiative transfer processes.Results indicate that NNFO is 15 or 6 times faster than RTTOV-DOM is in serial or parallel modes.The mean difference,RMSE,and mean absolute error

关 键 词：风云四号 A 星 先进静止轨道辐射成像仪 可见光反射率 RTTOV 数据同化 观测算子 神经网络 动态学习率 

分 类 号：P2[天文地球—测绘科学与技术]