| Aerosol is the important atmospheric ingredient, it influences both the climate change and water cycle, in the same time, by absorbing and scattering of electromagnetic wave, it can change the accuracy of quantitative remote sensing. The particles which have the smaller size can be absorbed into the body through breathing and influence the health. Trying to research the distribution of aerosol, investigate how it change the climate, as well as the radiative transfer, there are many research has been developed in the world wide. At present, the observation in our country is based on the ground based station and does not develop into the regional analysis. The aerosol suspend on the ground does not invariable and always change, commonly, aerosol is usually influence by the atmospheric circulation and the aerosol particles still diffusing and transporting. In the end, the station observation is limited. In this paper, the data is from spaceborne lidar, different from the passive remote sensor, lidar is an active remote sensor. Start from the data processing improvement, then analyzing the aerosol characteristics and the feature of layers.At present, the ground based lidar has been widely used, and it is the mature technology, but as the new method used for atmosphere observation, the algorithm of spaceboren lidar is different from the ground based lidar. Based on the official method, this paper study the pre-processing of spaceborne lidar retrieval, and give the improved scheme, all of these can choose a more appropriate lidar ratio, and support the correct retrieval. After that, used the atmospheric parameter from new processes analyze the distribution and aerosol optical depth in different area, China.At first, in this paper we give the method of data fusion between CALIPSO and CloudSat, and discriminate the layer suspends in the atmosphere which is the aerosol and which is the pseudo layer (cause by noise). Though introduce the CloudSat, which is also a satellite of A-train constellation, it is almost synchronous with CALIPSO. In view of the lower SNR (signal noise rate) of CALIOP observation and the layers signal below thick cloud is difficult to discriminate, the data fusion between them can avoid error layer detection, to lay the foundation for the subsequent analysis. Based on this improvement, consider the deficiency of scene classification about cloud and aerosol, the former algorithm employ the PDF (probability density functions), but it needs many samples and the parameter estimation is difficult. Further, for the effective validation of classification, we also introduce another remote sensor, MODIS. MODIS load on Aqua satellite which is also an A-Train satellite, the interval between them is less than one minute.Because the dust storm is a problem exists in our country, the appearance of dust storm is still serious, this paper analyzes the dust storm which happens in day time and conquer the influence from lower SNR. The result shown that, if the correct samples we choose, then the result is satisfactory, through calculate the particle size and shape, the differences between dust aerosol and ice cloud is known.In the part of data application, we use the Hybrid Extinction Retrieval Algorithms (HERA), and receive the aerosol characteristics of China. From the retrieval result, the aerosol optical depth, and their distribution of each layer is obtained. The result shown, aerosol distribution in Hubei province doesn't always in according with the ground surface, not the higher aerosol optical depth value above megalopolis. Further, the aerosol optical depth in Beijing is debased obviously, this means for the Olympic Games in 2008, the air quality in the urban areas picked up gradually. In the last part of the paper, the data fusion between MODIS and CALIPSO retrieval data used to analyze the aerosol optical characteristic in the Southeast China, this step can improve the accuracy and give more detailed information. The aerosol concentration in winter is lower than in summer.
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