Aerosol Classification Method And Optical Properties Research Based On Ground-based Observation Dat

As an important component of the atmospheric composition,aerosols have a significant impact on the global climate.Aerosols are highly spatial and temporally variable,with the size,shape and composition of their particles varying with the source of emissions.They affect climate change by absorbing and scattering solar radiation to change the balance of the earth’s income and expenditure,so it is important to study the differences of aerosol types in different regions and seasons to improve the climate environment.With the development of data mining,classification algorithms based on machine learning are gradually being widely used.In this paper,we apply the plain Bayesian algorithm to the aerosol classification problem and propose the Naive Bayesian classifier(NBC)aerosol recognition model with better recognition performance,and the research in this paper is organized as follows:(1)An NBC model was used to classify aerosols over the Great Plains sites in the southern U.S.based on sun photometer inversion data into urban industrial,biomass burning,dusty,marine,and mixed types.The model generates a classifier by learning the class probability distribution of the samples,and predicts the posterior probability based on the calculated class prior probabilities and conditional probabilities.The class corresponding to the maximum value of the posterior probability is the output of the sample to be measured,and the overall recognition rate of the constructed NBC model is 91% with the given reference standard as the training basis.(2)The quarterly mean profile distribution characteristics of aerosol recession ratio,lidar ratio and color ratio at vertical height are analyzed by taking advantage of the high accuracy detection of High Spectral Resolution Lidar(HSRL).The aerosol data matching criteria of two different observation means,sun photometer and HSRL,are set so that the aerosol clusters observed by both are approximately the same,and the threshold interval based on HSRL inversion data is established to determine the aerosol type,which is used to verify the classification results of NBC model.Experiments show that the NBC aerosol classification model proposed in this paper has better recognition performance than the traditional algorithm.(3)A typical aerosol event(dust event)is used as an example to focus on the change of aerosol optical properties.Using multiple observations,it is shown that several meteorological elements in the atmosphere as well as the aerosol optical properties change significantly at different times and different vertical heights with the increase of irregular coarse particles of sand and dust.The daily average values of temperature decreased and the daily average values of relative humidity and wind speed increased during the occurrence of sand and dust.The aerosol backscattering coefficient,extinction coefficient,optical thickness and particle size increased,and the number concentration level decreased.