The atmospheric channel characteristics of laser communication ground station determine the equipment performance. Four candidate sites, Ali in Tibet, Delingha in Qinghai, Daocheng in Sichuan, and Xinglong in Hebei China, were selected for statistical inspection of the effects on laser communication, including the factors of cloud coverage, atmospheric transmittance, sky background radiation, and atmospheric turbulence. It was shown that Ali is the best for single station availability; Ali-Delingha-Xinglong is the best for the three-station combined availability, more than 85% in full year under 60% cloudiness. For the four stations under 60% cloudiness, the combined availability can be 91%～96% in May to August, and reaches more than 98% in other months. The atmospheric optical properties of the four candidate sites meet requirement of laser communication. The high altitude sites have obvious advantages in atmospheric transmittance and background radiance. The atmosphere coherence length in night is larger than 10 cm in Ali and Daocheng, and 8～12 cm in Delingha, and just higher than 6 cm above for Xinglong. In multi-station combined mode, good sites, with sufficient high availability and meeting optical communication requirements, could be selected in high altitude and less cloud areas.

Lidar observation network plays an important role in the research of regional distribution characteristics of particulates. The data of the lidar network in the Yangtze River Delta region were examined together with meteorological data, PM2.5 and PM10 mass concentrations and the HYSPLIT backward trajectory model, to analyze the sources and causes of particle pollution in Hangzhou and surrounding areas. The analysis showed that, the particles pollution process in September 2016 in Hangzhou was the consequence of combination of the local pollution and external transportation dust, and the coarse particles were mainly from the northwest China in the pollution process. The concentration of SO2 remained at low level, and that of PM2.5 was positively associated with the concentration of NO2, and the nitrate second particles were prevalent in fine particles. The higher concentration of NO2, high humidity, and low wind speed were all the main causes of the rapid growth of fine particles in the region.

Particulate matter is one of the major pollutants in the port area. The multifractal theory was used to analyze the multifractal characteristics of the concentrations of PM2.5 and PM10 in the port area. Firstly, the multifractal characteristics of PM2.5 and PM10 were analyzed by Multifractal detrended fluctuation analysis(MF-DFA). The results showed that the evolution of PM2.5 and PM10 shows nonlinear and complex multifractal characteristics, and the multifractal characteristics of PM10 is more than that of PM2.5. Second, Multifractal detrended cross-correlation analysis(MF-DCCA) was used to study the cross-correlation of PM2.5 and PM10 in the port area. The results showed that there existed not only multifractal characteristics with long-term memory between them, but also obvious seasonal variation for its cross-correlation multifractal features. The multifractal characteristics of PM2.5 and PM10 in the port area are the strongest in spring, the second strongest in summer, and the weakest in autumn. These conclusions provide some reference value for the joint control of port area PM2.5 and PM10.

In order to establish an accurate and efficient air quality forecasting system, three forecasting models based on pollutants, meteorological factors and pollutant mixed meteorological factors were established and used as input variables for support vector machine regression (SVR) for the daily forecast to look for the best predictor mode. The support vector machine regression model was optimized by using grey wolf optimization (GWO) to form the GWO-SVR model. The experimental results show that the meteorological factors of pollutants mixing acted as input variables is the optimal predictor model, and the determination coefficients of the test set of SVR and GWO-SVR model are R2=0.79 and R2=0.81, respectively, which indicates both of the models have high prediction accuracy. By comparision, GWO-SVR model prediction performance is better. After that, the data is classified according to the wind direction conditions and the better GWO-SVR is used to predict the PM10 concentration. The prediction results show that when the prevailing southwest wind prevails, the evaluation index of prediction set is R=0.91 and MSE=47.15, which is better than the status with prevailing northeasterly wind where R=0.87 and MSE=125.80 and the whole data with R=0.90 and MSE=107.94.

The compositions of fog and haze particles are different. The droplets have the characteristics of large particles, while the composition of haze is complex, and small particles are mostly present. Polarization observation has very great sensitivity to small particles. Based on this characteristic, the polarized radiation characteristics of fog and haze particles are calculated using the vector linearized discrete ordinate radiative transfer(VLIDORT) model. The simulation results can be used to obtain the polarization reflectance of fog and haze particles. In the range of scattering angles of 125° to 150°, the polarization reflectance of fog and haze particles varies significantly with the scattering angle distribution. By taking advantage of the radiation characteristics of fog and haze particles, fog and haze have been distinguished based on the polarization and directionality of the earth’s reflectances(POLDER)data on March 11, 2008. The distinguished results were compared with moderate-resolution imaging spectroradiometer (MODIS) images and ground station observation data to verify the effectiveness of the algorithm. It is believed that the alogorithm has great reference value and scientific significance for China’s GFof directional polarimetric camera(DPC) remote sensing data.

Temperature measurement is significant for combustion diagnosis. An on-line temperature measurement method based on tunable diode laser absorption spectroscopy(TDLAS) has been developed, which achieves dual laser cooperative work and simultaneous measurement of two absorption lines (7154.35 cm-1 and 7467.77 cm-1) of combustion product H2O by dual beam time dividing scanning strategy. The accurate inversion of the temperature is realized by two-line integral absorbance ratio, which meets the application needs of on-line temperature detection of the combustion field. The experimental research on real-time detection of flame temperature in CH4/air premixed flame furnace was carried out, and the results were compared with the thermocouple measurement, which shows TDLAS and thermocouples have good consistency with relative error less than 3.8%. The feasibility and reliability of non-intrusive and rapid temperature measurement of the combustion field based TDLAS technology have been verified.

In colorimetric temperature measurement, high temperature background radiation interferes with the accurate measurement of target temperature. Monte Carlo method is used to simulate the emission position, radiation direction, reflection and absorption characteristics of background radiation by probability distribution. The radiant energy beam reflected by the target was detected by the detector and compared with the theoretical radiation difference to verify its accuracy. On this basis, a method that can eliminate the influence of high-temperature background radiation in a complex environment and improve the accuracy of temperature measurement is proposed.

Through acquiring high accurate UV/Vis radiation scattered or reflected by air or earth surface, atmospheric trace gas differential absorption spectrometer can monitor distribution and variation of trace gases based on differential optical absorption spectrum algorithm. The laboratory calibration of the load is performed on the ground to verify the performance and reliability of the load and support the data processing of the load. In order to quickly process load calibration data, spectral calibration and radiation calibration software was developed. Calibration software is based on C++ language and developed on Windows platform to achieve data calibration and multi-dimensional display calibration data. Polynomial fitting algorithm is used in spectral calibration, while Gauss fitting algorithm is used in spectral resolution. Radiation calibration includes absolute radiation calibration and relative radiation calibration. Absolute radiation calibration is used to determine the instability and non-linearity of radiation calibration. Relative radiation calibration is used to obtain relative radiation correction coefficients and uncertainty. The results show that the software runs well and is competent for the processing the load ground spectrum calibration and radiation calibration data.

A new generation of stationary orbit meteorological satellite Himawari-8 AHI is a multispectral earth observation sensor with high temporal and spatial resolutions, which is of great significance to the monitoring of sea fog. The vertical structure of the atmosphere at high-resolution can be detected by cloud-aerosol lidar with orthogonal polarization(CALIOP). CALIOP data were used to classify the observed objects into four types: sea-surface, sea fog, stratus and middle/high-cloud. These samples were then used to the spectral analysis of the Himawari-8 channels and the spectral properties were obtained. A daytime sea fog detection algorithm based on Himawari-8 data was proposed through the study of the spectral properties. Both CALIOP data and fog-monitoring product from NSMC were used to validate the detection results. Compared with the CALIOP fog detection, the probability of sea fog detection is 63.42%, and 71.54% of the missing samples are the misjudgements of sea surfaces. Based on the fog-monitoring product, the probability of sea fog detection is 89.7%. The results show that the proposed algorithm is feasible.