面向星地激光通信的大气湍流预报研究进展(特邀)  

Atmospheric optical turbulence prediction method for satellite-ground laser communication(invited)

作　　者：郭盈池 李浪 李晨 高春清[1,2,3] 付时尧[1,2,3] Guo Yingchi;Li Lang;Li Chen;Gao Chunqing;Fu Shiyao(School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China;Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education,Beijing 100081,China;Key Laboratory of Information Photonics Technology,Ministry of Industry and Information Technology,Beijing 100081,China)

机构地区：[1]北京理工大学光电学院,北京100081 [2]光电成像技术与系统教育部重点实验室,北京100081 [3]信息光子技术工信部重点实验室,北京100081

出　　处：《红外与激光工程》2024年第3期19-31,共13页Infrared and Laser Engineering

基　　金：国家自然科学基金项目(62375014,62350011,11834001,61905012);国家重点研发计划(2022YFB3607700);北京市自然科学基金项目(1232031)。

摘　　要：激光通信系统在大气环境下应用的性能受到严重制约,当激光在大气湍流中传输时,其波面会发生畸变。在激光通信系统中,对大气湍流相关参数进行预报可以提前对星地数据传输链路进行调度,避免建立无效的通信任务。此外,大气湍流预报在天文选址、光学遥感等领域也有重要价值。随着国内外相关工作的长期积累以及算力、观测设备等硬件的能力提升,当前科研人员已经提出了一些大气湍流预报方案。文中主要综述了国内外学者在大气湍流预报方面的研究进展,首先详细介绍了目前应用比较广泛的中尺度数值预报技术,列举了使用中尺度数值预报方法对国内外典型区域的大气湍流进行预报的相关工作;然后介绍了深度学习方法在大气湍流预报中的应用情况,对其优势与局限性进行了讨论;最后介绍了一种面向星地激光通信的大气相干长度短时预报方法。Significance The prediction of atmospheric turbulence has great significance both in science and engineering,which provides key parameters and references for domains like astronomical observation,site selection,satelliteground laser communication,and remote sensing.Especially in satellite-ground laser communication,predicting key parameters of atmospheric turbulence can schedule satellite-ground data transmission links in advance,and pre-deploy adaptive optical schemes to compensate turbulence effects,so as to establish effective communication links and suppress the performance degradation of data transmission.Therefore,atmospheric turbulence prediction is crucial and become an important issue,which needs to be addressed for most of laser scenarios in atmosphere.Progress This review consists of three sections.In the first section,firstly,the widely used meso-scale numerical prediction scheme to forecast atmospheric turbulence is introduced in detail.This scheme is accomplished by turbulence parameterization schemes,which establishes the relationship between the turbulence characteristics and the conventional meteorological parameters output from mesoscale meteorological model.Mesoscale meteorological model has been well developed,the most representative models include Meso-Nh(Nonhydrostatic mesoscale atmospheric model),MM5(Mesoscale Model 5),WRF(Weather Research&Forecasting Model)and Polar WRF.Many achievements have been made in turbulence parameterization schemes,including Hufmagel model,Tatarski model.Then,the relevant work of using mesoscale numerical prediction method to forecast atmospheric turbulence in typical regions is reviewed.The second section presents recent advances regarding deep learning in atmospheric turbulence prediction,and discusses its advantages and limitations.This section first introduces the research achievements of deep learning in meteorological forecasting,and then introduces the research advances of deep learning in atmospheric turbulence forecasting.Based on a large amount of data,d

关 键 词：大气湍流 湍流预报 Cn2廓线 大气相干长度 

分 类 号：TN929.12[电子电信—通信与信息系统]