Atmospheric detection lidar is a powerful tool with fine temporal-spatial resolution, high detection accuracy and real-time profiling data acquisition. Lidar network and spaceborne lidar technology, can obtain large-spatial-scale and continuous four-dimensional atmospheric data for researching on environment, meteorology and climate. The lidar network and the spaceborne lidar program are introduced in detail. The main lidar networks include the network detection of atmospheric composition change(NDACC), the European aerosol research lidar network(EARLINET), the Asian dust and aerosol research network(AD-NET), the regional east atmospheric lidar mesonet(REALM), micro-pulse lidar network(MPLNET), and the atmosphere aerosol and ozone monitoring in CIS regions(CIS-LINET). The functions, the lidar types organization and lidar sites, daily observation activities and standardization are described respectively for every lidar network. Lidar in space technology experiment(LITE) has opened a new era for spaceborne lidar. Following that, several spaceborne lidar programs are proposed by the NASA, ESA and JAXA. The scientific goal, lidar type, lidar related parameters and the technical principles of these spaceborne lidar are introduced respectively. In China, a multifunction spaceborne lidar is also proposed to monitor aerosol, cloud and CO2. In summary, the advantages and the applications of the lidar networking observation and the spaceborne lidar are illustrated.

The background and meaning of developing the technique of fold pass laser imaging and detection for atmospheric optical turbulence are analyzed, and the physical processes and mathematic models of fold pass laser transmission are represented. Using the constructed experimental system, the echoed dynamic laser speckle images for man-made air flow and natural turbulence flow are obtained. The typical characteristics of the images are described. Calculation of the images with the algorithm of low pass filtering show that the images can be decomposed into low frequency shadows and high frequency bright dots. The mutual correlation of the adjacent shadows can produce the two dimensional vector winds and then visualize the vortexes in the turbulence field. Meanwhile, the anisotropy in the turbulence field can be disclosed. Some scientific problems such as how to extract turbulence scalar parameters from the vector wind, and how to retrieve the structure of optical turbulence from the bright dots in the speckles combined with the principle of background oriented schlieren are proposed, which would be investigated in the next step.

Research of atmospheric turbulence is the actually investigation of atmospheric refractive index. The atmospheric structure constant of refractive index C2n is an important parameter of denoting atmospheric turbulence. Using new micro-thermal meter, the atmosphere temperature, pressure and C2n are measured in Hefei of Anhui, Beijing and Kuerle of Xinjiang by analyzing experimental sounding data in different representative area, the statistical characteristics of vertical distribution of atmospheric parameter are investigated to offer necessary data for atmospheric turbulence model. Using Hufnagel-Valley model and NOAA model, the atmospheric turbulence models in different representative area are obtained on the basis of actual upper air data. Method evidence and valuable reference will be provided for research on atmospheric turbulence mode in other typical area.

The identification of water quality is an important prerequisite for accurate spectroscopic detection of water quality parameters. Aiming at the problem of large redundancy of spectral data collected by direct spectrum water quality detection system, the principal component analysis is used to eliminate the correlation of information indexes, and the spectral data is reduced and the feature information is extracted. The UV-Vis spectra of water from a chemical plant and a stream were collected. The discriminant model was established by using the method of principal component analysis and Fisher discriminant. First, 12 sets of water samples were used as training samples and 6 groups as test samples. Then, the discriminant ability of the model was demonstrated and tested, and compared with the traditional Fisher discriminant model. Finally, The experimental results show that the joint Fisher discriminant model can effectively eliminate the influence of information redundancy. Compared with the traditional Fisher discriminant model, it has the advantages of high classification precision, zero return error rate and short calculation time. The calculation time is reduced from 0.6733 s to 0.6012 s. This method provides an efficient means for practical application of direct spectrum method to determine the water quality.

Most pollutant gases can be detected by passive Fourier transform infrared(FTIR) remote sensing technique, and brightness temperature technique can implement the extraction and identification of target feature without the premise of background information. There usually exist radiation of background objects, radiance of atmospheric composition, and noise from the instrument itself in field measurements. When the target signal is weaker than these signals, the target feature can’t be extracted directly from the measured spectra by the brightness temperature technique. Accordingly, an extraction method of spectra feature from measured spectra based on non-negative matrix factorization(NMF) is proposed. It can get the target feature through the analysis of the brightness spectra on the basis of passive Fourier transform infrared remote sensing. The experiment is conducted with SO2 as the target gas. The method is verified with the measured spectra. Results show that the feature of SO2 can be extracted even if the target signal is weak. The validity of the method is proved.

Based on the cloud image data of a new generation of stationary orbit meteorological satellite Himwari-8 AHI, the modified TV norm optical flow method is put forward to reverse the cloud derived wind. Firstly, through the ROF denoising model the image details are extracted. Using TV-L1 norm local penalty, the optical flow method is presented to achieve a high resolution cloud derived wind inversion. The quality of cloud derived wind is controlled based on quality identification. And unreasonable wind vector is removed. The experimental results show that this method is suitable for cloud derived wind of the low and medium cloud level under the height of 400 hPa.

Clouds are related to life closely, and research on cloud parameters is important, including the accurate identification of cloud phases. Because the traditional single-sensor-based cloud phase identification algorithm has certain limitations, a new method for cloud phase detection combined with CloudSat-CALIPSO-MODIS multi-sensor is proposed, which improves the accuracy of cloud phase recognition. Using the comprehensive observation data of CloudSat, CALIPSO and MODIS on May 2, 2008 and February 1, 2010, six cloud phases were obtained, including uncertain cloud, hybrid cloud, water cloud, supercooled water cloud, ice cloud and clear skies. The results show that using the synergistic algorithm, the cloud phase can be distinguished more accurately, which provides conditions for numerical weather prediction and has important scientific significance.