摘要
针对气象领域中仿真云图生成问题,提出一种基于深度生成对抗网络的仿真卫星云图生成方法。利用深度学习的非线性映射能力和对栅格数据的信息提取能力,选取合适的数值模式产品要素作为输入,建立深度生成对抗模型提取同时次、同区域数值模式产品和卫星云图产品的对应有效信息,再利用提取的信息将数值模式产品重构为卫星云图产品。基于欧洲中期天气预报中心数值模式再分析场产品和风云4A气象卫星产品的实验表明,所提方法可以有效的将数值模式产品重构为卫星云图仿真产品。
To create the synthetic satellite cloud data in the domain of Meteorology, a method based on Generative Adversarial Networks(GAN) is proposed. Depending on ability of the nonlinear mapping and the information extraction of raster data with the deep learning network, a deep generative adversarial network model is proposed to extract the corresponding information between the numerical weather prediction(NWP) products and the satellite cloud data, and then the appropriate elements of the NWP product are chosen as the input to synthesize the corresponding satellite cloud data. The experiments are conducted on the re-analysis products of the European Centre for Medium-Range Weather Forecasts(ECMWF) and FY-4 A satellite cloud date.The results show that the proposed method is effective to create synthetic satellite cloud data by using the NWP products.
作者
程文聪
史小康
王志刚
Cheng Wencong;Shi Xiaokang;Wang Zhigang(Air-Force Research Academy,Beijing 100085,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2021年第6期1297-1306,共10页
Journal of System Simulation
基金
高分对地观测专项(GFZX0402180102)。
关键词
深度学习
生成对抗网络
数值模式产品
卫星云图
仿真
deep learning
generative adversarial networks
numerical weather prediction products
satellite cloud data
simulation
