Forward model for O2 A-band night glow limb radiance in the mesophere and lower thermosphereWang, Weijia; Luo, Haiyan; Li, Zhiwei; Xiong, Wei
doi: 10.1117/12.2682207pmid: N/A
The temperature structure of the global mesosphere and lower atmosphere (MLT) is significant for the study of atmospheric physical, chemical and kinetic processes. Oxygen (O2) A-band airglow (762 nm) can be used as an important tracer to detect the atmospheric temperature structure. The advantage of spatial heterodyne spectrometer (SHS) is high stability, high throughput and high spectral resolution. The fine spectral structure of A-band night glow is detected by limb observation combined with simultaneous split field imaging of atmospheric vertical profile, and the temperature information is retrieved by recovering spectra. Building an accurate forward model is the premise and foundation to obtain the global spaceborne high-resolution atmospheric temperature structure. Based on the A-band night glow radiation mechanism, molecular spectroscopy theory, atmospheric radiation transfer theory and the detection principle of SHS, this paper constructs the forward model of target airglow observation. Furthermore, the sensitivity and analysis of the influencing parameters of the forward model is carried out, which provides a theoretical basis fort the forward modes modification and instrument design. The results show that the forward model described in the paper can satisfy the simulation of A-band night glow spectral radiance observed by SHS at any location through limb observation combined with simultaneous split field of atmospheric vertical profile. That is, the atmospheric tangent range covers 80-120 km and the vertical resolution is better than 2 km. It lays a foundation for the space-borne SHS to detect and accurately retrieve the global temperature structure in MLT region.
Restoration of rotating Fourier transform ultraviolet Raman spectrum with a time sequences trigger methodGuo, Haitao; Tang, Ming
doi: 10.1117/12.2683101pmid: N/A
Rotating Fourier transform ultraviolet Raman spectrometer (RFTURS) is an ideal and valuable tool for atmospheric CO2 detection. However, due to the short excitation wavelength and the nonlinear relationship between the optical path difference (OPD) and the rotating angle, it is difficult for RFTURS to produce linear interference signals. This paper proposes a time sequences trigger (TST) method to realize linear sampling of interference signals. Specifically, time sequences are designed at equidistant OPD intervals as external clocking signals to trigger the data acquisition (DAQ) card to sample interference signals. High angular resolution and precise time sequences are obtained by using a gearbox to work at low speed as well as detecting the OPD zero via a sensor, respectively. By a simulation spectrum and white light, the method was tested and ultimately obtained the expected restored spectrum, which addresses the current drawbacks such as large error of the non-uniform fast Fourier transform (NUFFT) method and the subsampling method, and unsuitable work conditions of the reference laser method performing at the UV. This work provides significant guidance for field measurement of atmospheric CO2 by RFTURS.
Coherence analysis of laser pulses based on an external cavity circulationXu, He; Sun, Difeng; Li, Jianbing
doi: 10.1117/12.2682203pmid: N/A
By constructing an external circulating cavity to provide sufficient delay that equals a multiple of pulse repetition time, a method with a fixed experimental configuration is proposed to measure the coherence length of both single-frequency and microwave-modulated optical pulses. This method can accurately determine the number of coherent pulses as well as distinguish the coherence states: complete coherence, partial coherence and complete incoherence. In addition, all desired coherence phenomena are obtained by one-time measurement, avoiding other operations like frequent fiber-cutting or devices-reconnection in previous methods. Simulation results show that the coherence length of the dual-frequency laser is periodically extended by the reciprocal of the frequency difference, and the random jitter of pulse propagation time would result in obvious measurement errors via perturbing coherence status.
A method for predicting aerosol optical thickness based on kernel principal component analysis using geographically and temporally weighted regression modelWang, Yue; Ye, Hanhan; Wang, Xianhua; Shi, Hailiang; Wei, Xiong; Li, Chao; Sun, Erchang; An, Yuan; Xiang, Kunzhu
doi: 10.1117/12.2682060pmid: N/A
The spatial and temporal distribution of atmospheric aerosols closely affects climate change, air quality, environmental pollution and human health. Exploring and predicting the spatial and temporal characteristics of regional atmospheric aerosols is beneficial to the monitoring and assessment of regional atmospheric environmental quality. Taking the Qinghai-Tibet Plateau and its surrounding areas as an example, this study considers the spatial and temporal non- stationarity of aerosol optical thickness (AOD) and its multiple driving factors, and proposes a geographically and temporally weighted regression method based on kernel principal component analysis (KPCA-GTWR) is proposed. The method eliminates the multicollinearity among the driving factors after the multicollinearity test, extracts the principal components with a cumulative contribution rate greater than 95% as the input of GTWR, and improves the prediction accuracy of GTWR. Finally, the method compared with the prediction results of the conventional VIF-GTWR method and PCA-GTWR method. The results found that (1) there are correlations between AOD and its multiple drivers, as well as linear and nonlinear correlations between the drivers. (2) In comparison, the KPCA-GTWR method has the highest prediction accuracy. Compared with the conventional VIF-GTWR and PCA-GTWR methods, the predicted AOD with MERRA-2 AOD 10-fold cross validated R 2 improved from 0.764, 0.861 to 0.914 , RMSE decreased from 0.059, 0.05 to 0.044 , and MAE decreased from 0.043, 0.037 to 0.033, respectively. (3) Comparing the results in June, July, August in 2020, the spatial distribution of AOD and MERRA-2 AOD predicted using this method in and around the Tibetan Plateau is consistent and shows large spatial differences. The low values of both predicted AOD and MERRA-2 AOD are located in the main part of the Tibetan Plateau, around 0.25 or less, while the high values are found in the Tarim Basin, the Ganges Basin of India and the Sichuan Basin, up to 0.75 or more.
The research progress in underwater wireless optical communication technologyLi, Peng; Han, Xiaotian; Nie, Wenchao; Chang, Chang; Li, Guangying; Liao, Peixuan; Wang, Wei
doi: 10.1117/12.2682867pmid: N/A
Underwater wireless optical communication, which is an effective technical way to build a high-speed and flexible underwater wireless communication network due to its large bandwidth, good confidentiality and low time delay, is widely regarded as a complementary communication way to hydroacoustic communication. The newest research progress and main technical specification in the current technology of underwater wireless optical communication are introduced, the characteristics of the current underwater wireless optical communication technology are summarized in this paper. A series of improvement measures are proposed for the problems of short communication distance and low robustness in underwater wireless optical communication system, which can provide references for underwater wireless optical communication technology research and engineering project development.
Precipitation estimation by infrared brightness temperature measurement of FengYun-4A imagerWang, Gen; Ye, Song; Yuan, Song; Jiang, Yun
doi: 10.1117/12.2682809pmid: N/A
In this paper, based on the infrared channel brightness temperature from the advanced geosynchronous radiation imager (AGRI) of FengYun-4A satellite, the research on quantitative estimation of precipitation is carried out. The algorithm of precipitation estimation can be divided into three steps. Firstly, the dictionary of brightness temperature of FY-4A/AGRI infrared channel brightness temperature and the integrated multi-satellite retrievals for GPM (IMERG) precipitation is constructed as the historical training sample library. Secondly, the precipitation FOVs are identified. As prior information, the IMERG and ice cloud products are coupled to classification models of the K-nearest neighbor (KNN) and random forest to determine whether there is precipitation at the FOV to be estimated. Finally, the precipitation estimation is performed. Inverse problem regularization method and random forest regression model are used for precipitation estimation, respectively. On this basis, the preliminary experiments for precipitation estimation of and “Ampil (2018)” are carried out. The results show that the precipitation estimation accuracy with ice cloud products as prior information through the inverse problem regularization is better than that with the IMERG products as priori information, while the conclusion is the opposite for the random forest method. The accuracy of precipitation estimation based on the random forest method is better than that of the inverse problem regularization, especially in the “extreme” precipitation center.
Prediction of aerosol scattering and absorption coefficients based on machine learningLiu, Menglei; Li, Xuebin; Wang, Feifei; Chen, Jie; Luo, Tao; Cui, Shengcheng; Zhang, Zihan; Liu, Qiang
doi: 10.1117/12.2682968pmid: N/A
Aerosol scattering and absorption coefficients are important parameters that characterize the optical properties of aerosols, which have significant impacts on the radiation balance, air quality, and climate change of the Earth. In order to further improve the understanding of the relationship between aerosol optical properties and meteorological parameters in the offshore areas of Guangdong Maoming, the scattering and absorption coefficients of aerosols as well as meteorological parameters such as temperature, humidity, pressure, wind speed, wind direction, and visibility were measured. In this study, a prediction model of aerosol scattering and absorption coefficients based on the CatBoost algorithm was proposed using the measured data. Firstly, the measured data was preprocessed, and then a CatBoost algorithm model based on ensemble learning was constructed and trained. The Optuna framework was used to optimize the hyperparameters of the model to obtain the final aerosol scattering and absorption coefficient prediction model. Finally, the machine learning model was used to predict the scattering and absorption coefficients of aerosols in the offshore areas of Maoming. The model was compared with XGBoost and LightGBM algorithm models, and the mean squared error (MSE) and mean absolute error (MAE) were used as evaluation metrics to assess the accuracy of the model predictions. Based on the evaluation metrics, the CatBoost algorithm model based on Optuna automatic hyperparameter optimization performed the best among several models. The experimental results showed that when the training and testing data came from the same region, the MAE of the CatBoost algorithm model based on Optuna hyperparameter optimization was about 5.33, and the MSE was about 48.764, achieving a prediction accuracy of 90.88% for aerosol scattering and absorption coefficients.
Fast detection method of calibrator spaceborne sun glint based on FPGALi, Yuhao; Ji, Feng; Hu, Yadong; Chi, Gaojun; Qiu, Zhenwei; Li, Zhuoran; Chen, Feinan
doi: 10.1117/12.2681857pmid: N/A
Sun glint is a serious obstacle to passive optical remote sensing images. As is the specular reflection of sunlight from the facets of the water surface, sun glint has great linear polarization characteristics, and it is usually suppressed by adding polarizers. Therefore, a spaceborne sun glint polarization parameter measurement system is developed to calculate the on-orbit sun glint parameters in real time. Firstly, we analyzed the polarizing radiation distribution model of sun glint and developed a real time detection system for polarization angle of sun glint according to the principle of spaceborne polarization imager. The system is using Xilinx V5 FPGA as the on-the-satellite processing platform, and we use high-level synthesis (HLS) tools for algorithm hardware description development. By using dataflow, pipeline and other optimization methods in HLS, we greatly reducing computing time and reducing the FPGA resource use. Finally, we use it to calculate the sun glint polarization angle through a three-channel data with a granularity of 25×25 in 670nm, the simulation results show that under the 100M clock, 54% of the slice and DSP48 FPGA resources are consumed, and the sun glint polarization angle can be calculated in 8ms time, which meets the design requirements of rapid sun glint detection.
Optical design of off-axis three-mirror reflective system by neural networksHuang, Wanqing; Sun, Xibo; Xie, Yu; Geng, Yuanchao; Liu, Lanqing; Wang, Wenyi; Zhang, Ying
doi: 10.1117/12.2682144pmid: N/A
We incorporate neural networks into the optical design of off-axis three-mirror reflective system, enabling us to achieve design outcomes without relying on iteration or ray tracing methods. Our approach involves combining analytical relations with neural networks during the design process, which yields results covering the entire parameter space with a single user input, and each design is scored simultaneously. Our results demonstrate that neural networks can simulate the complex relationship between performance requirements and structural parameters of an optical system. As such, the structural parameters can be directly obtained from the performance requirements, replacing the iterative optimization process traditionally used. This approach leads to relatively efficient and straightforward optical design. We anticipate that this method can be extended to various optical systems, reducing the experience threshold and difficulty of optical design.