Optical turbulence strongly affects different types of optoelectronic and adaptive optics systems. Systematic direct measurements of optical turbulence profiles [$C_n^2\left(h\right)$] are lacking for many climates and seasons, particularly in marine environments, because it is impractical and expensive to deploy instrumentation. Here, a backpropagation neural network optimized using a genetic algorithm (GA-BP) is developed to estimate atmospheric turbulence profiles in marine environments which is validated against corresponding [$C_n^2\left(h\right)$] profile datasets from a field campaign of balloon-borne microthermal measurements at the Haikou marine environment site. Overall, the trend and magnitude of the GA-BP model and measurements agree. The [$C_n^2\left(h\right)$] profiles from the GA-BP model are generally superior to those obtained by BP and the physically-based (HMNSP99) models. Several statistical operators were used to quantify the GA-BP model performance on reconstructing the optical turbulence profiles in marine environments. The characterization of vertical distributions of optical turbulence profiles and the main integral parameters derived from [$C_n^2\left(h\right)$] profiles are presented. The median Fried parameter, isoplanatic angle, and coherence time are 9.94 cm, ${0.69}^{″}$, and 2.85 ms, respectively, providing independent optical turbulence parameters for adaptive optics systems. The proposed approach exhibits potential for implementation in ground-based optical applications in marine environments.

Remote sensing of HDO and CH₄ could provide valuable information on environmental and climatological studies. In a recent contribution, we reported a 3.53 μm distributed feedback (DFB) inter-band cascade laser (ICL)-based heterodyne radiometer. In the present work, we present the details of measurements and inversions of HDO and CH₄ at Dunhuang, Northwest of China. The instrument line shape (ILS) of laser heterodyne radiometer (LHR) is discussed firstly, and the spectral resolution is about 0.004 cm⁻¹ theoretically according to the ILS. Furthermore, the retrieval algorithm, optimal estimation method (OEM), combined with LBLRTM (Line-by-line Radiative Transfer Model) for retrieving the densities of atmospheric HDO and CH₄ are investigated. The HDO densities were retrieved to be less than 1.0 ppmv, while the CH₄ densities were around 1.79 ppmv from 20 to 24 July 2018. The correlation coefficient of water vapor densities retrieved by LHR and EM27/SUN is around 0.6, the potential reasons for the differences were discussed. Finally, in order to better understand the retrieval procedure, the Jacobian value and the Averaging Kernels are also discussed.

The resolution and imaging quality of ghost imaging is determined by the longitudinal spatial coherence (LSC) of speckle beams on the signal and reference arms. Based on the cross-correlation function, long-exposure and short-exposure computational ghost imaging through turbulent atmosphere is investigated analytically and numerically in the framework of the traditional imaging theory. According to the point spread function (PSF), the modulation transfer function (MTF) is derived, both of which are utilized to evaluate imaging resolution and imaging quality of computational ghost imaging (CGI), respectively. By simulating long-exposure and short-exposure ghost imaging through atmospheric turbulence, the comprehensive effects of atmospheric turbulence and beam initial parameters on the complex degree of coherence (CDC), PSF, and MTF are studied, respectively. It is found that the degradation of LSC between the two planes on the reference and signal path implies a narrower PSF and increased MTF values, which represent the better resolution and imaging quality. Thus, reducing the atmospheric turbulence strength, the speckle particle size, the wavelength and the propagation distance, and increasing the source size contribute to improving resolution and image quality of CGI because of the degradation of LSC. Furthermore, short-exposure CGI can provide imaging performance superior to long-exposure CGI in terms of resolution and imaging quality due to the decrease of LSC.

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future.

Based on the extended Huygens–Fresnel principle and infinitely long phase screen, the scintillation index and the aperture averaging effect of partially coherent beams in computational ghost imaging (CGI) with a finite bucket detector through atmospheric turbulence is investigated analytically and numerically. The signal–to–noise ratio (SNR) is used to evaluate the image quality of computational ghost imaging. It is found that a strong phase modulation effect due to increasing turbulence intensity, leads to a degradation in image quality, as well as an increase in the scintillation index. In addition, the scintillation–saturation phenomenon occurs for strong turbulence. On the other hand, reducing the propagation distance, and the degree of source coherence results in a decrease in the scintillation index and an improvement of image quality. However, deteriorating the degree of beam source coherence could weaken the aperture averaging effect. Thus, the optimal beam and bucket detector aperture size require a trade–off between the scintillation index, the aperture averaging effects, and the image quality in CGI.

调制传递函数（MTF）定量描述混浊介质对图像质量的影响，它是混浊介质的固有光学特性，利用等效原理可以获得MTF从低频到高频的完整特征.在实际应用中，混浊介质中的图像质量不仅取决于介质的MTF，还与图像的背景辐射密切相关.本文从混浊大气中图像退化机理出发，理论分析了空间频域中天光背景下图像质量的退化过程.参考等效原理，提出了一种考虑天光背景的表观MTF，得到了表观MTF与介质MTF和天光背景的定量关系，从而得到了一种分析背景辐射下混浊介质中图像质量的有效便捷方法.针对图像质量优化方法，从空间频域的角度提出了一种评价原则.