Atmospheric optical turbulence prediction method for satellite-ground laser communication (invited)
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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。
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关键词:
- 大气湍流 /
- 湍流预报 /
- $ {C}_{n}^{2} $廓线 /
- 大气相干长度
Abstract: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, satellite-ground 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(Non-hydrostatic 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, deep learning scheme can establish a relationship between the input data and the target label without any prior formula. In atmospheric turbulence prediction, deep learning is used to establish the relationship between meteorological parameters and atmospheric turbulence parameters, but the prediction accuracy is also limited by the accuracy of meteorological parameters. In the third section, a short-time atmospheric coherence length prediction method called TsVMD-AR is introduced. TsVMD-AR model uses VMD (variational mode decomposition) algorithm and AR (autoregression) algorithm to forecast the short-term atmospheric coherence length. This scheme reduces the interference and coupling between the multi-scale feature information in the dataset, makes the complex internal features of the dataset easier to obtain. The results show that the established TsVMD-AR model is obviously superior to other models and is suitable for daily atmospheric turbulence prediction. Prospects We hope this review will provide more valuable information for people who is working in scenarios of laser applications in atmosphere turbulence, and inspire more wonderful ideas towards abilities of more accurate and faster turbulence grasp. -
图 1 大气湍流的中尺度数值预报。(a) Cerro Paranal台址模型计算结果与实测$ {C}_{n}^{2} $廓线的对比[33];(b) WRF预报系统工作流程图[25];(c) 高美古WRF预报结果与实测结果的比较
Figure 1. The meso-scale numerical prediction of atmosphere turbulence. (a) Results comparison between the model of Cerro Paranal station site and the measured $ {C}_{n}^{2} $ profiles[33]; (b) WRF modeling system flow chart[25]; (c) Comparison of WRF and measured data at Gaomeigu
图 4 基于深度学习的大气光学湍流预报方案。(a) BP算法网络结构[87];(b) AGA-BP 神经网络结构[89];(c)实测值、AGA-BP 网络预报值、Polar WRF预报值的比对结果[89]
Figure 4. Atmospheric optical turbulence prediction through deep learning. (a) BP neural network[87]; (b) AGA-BP neural network[89]; (c) Results of measured data, Polar WRF prediction, AGA-BP neural network prediction[89]
图 6 TSVMD-AR模式和其他方法的湍流预报结果对比结果[98]。(a)实测湍流(蓝色)和TsVMD-AR(红色)、SARIMA(绿色)和WRF(灰色)预报结果;(b)~(d)观测到的大气湍流二维直方图与TsVMD-AR(b)、SARIMA(c)和WRF(d)的预报结果对比
Figure 6. Turbulence forecasting results of the TsVMD-AR model and the other schemes[98]. (a) Observed atmospheric turbulence results (blue), and forecasting results through TsVMD-AR (red), SARIMA (green) and WRF (grey); (b)-(d) 2D histograms of observed atmospheric turbulence result verse forecasting results of TsVMD-AR(b), SARIMA(c) and WRF(d)
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