Application Of Aerosol Data Assimilation Of New Generation Meteorological Satellites In Air Pollution Simulation

With the improvement of urbanization scale and level,China's economy has developed rapidly.At the same time,the problem of air pollution is becoming more and more serious.The simulation and prediction of air pollution has gradually become a hot social problem and a scientific problem.With the improvement of computing power and the development of model prediction,the air quality forecast model has gradually become the main research method in the field of air pollution.Data assimilation technology can provide accurate initial conditions for forecasting models.It is an effective method to reduce the uncertainty of model forecast.Aerosol plays a very important role in climate system and environmental pollution.In recent years,with the continuous progress of satellite remote sensing technology,atmospheric detection technology has become an important way to obtain atmospheric aerosol data in the field of atmospheric research.Making full use of the new generation of advanced meteorological satellite aerosol data is a hot spot in the current air quality forecast research.In this study,by combining the WRF-Chem prediction model and using three-dimensional variational method in the GSI system,the assimilation system which can assimilate the aerosol data from Fengyun-3,Himawari-8 and Fengyun-4 satellites has been constructed respectively.The improvement effect of the assimilation experiments on the aerosol forecast was further verified by applying it to the actual cases of air pollution.The results show that:(1)Based on the GSI assimilation system,a three-dimensional variational assimilation system for the aerosol data from Fengyun-3 satellite was constructed.WRF-Chem model was used for aerosol forecast,and then the ability of the model to forecast aerosol after data assimilation experiments was tested.The NMC method was used to calculate the background error covariance matrix,which well reflected the vertical characteristics of 14 aerosol variables.After assimilation,the bias and RMSE of AOD value are optimized by nearly 30%,which further verifies the positive influence of AOD data assimilation system.With the addition of satellite data,the analysis field provides more abundant dust weather information.FY-3A assimilation experiment performed better than MODIS satellite data assimilation experiment.The improved effect of FY-3A aerosol data assimilation on model prediction is further verified.(2)To further improve the performance of the assimilation system,based on the characteristics that the geostationary satellite can continuously observe the same area,a rapidupdate data assimilation system based on the geostationary meteorological satellite Himawari-8 satellite aerosol data was constructed.For the first attempt,the aerosol data from Himawari-8 satellite were introduced into the GSI analysis system,and then applied to a severe dust storm in May 2017.The experiment of assimilating AOD data from AHI satellite has a good effect on the simulated strength and coverage of AOD value,especially in the area near the center of large value in northeast China.Compared with the Aeronet data,the "AHI 1HDA" assimilation experiment showed the most significant improvement and was closer to the ground observation station than the other experiments.It may be because the assimilation of high frequency data helps to provide rich initial field information.The effectiveness of the assimilation system was fully tested through the real weather case.(3)Based on the rapid-update assimilation system constructed in the GSI,the aerosol data from Fengyun-4,the second generation of China's geostationary meteorological satellite,was introduced into a dust storm case,and its influence on the aerosol prediction was systematically compared.After the addition of satellite AOD observations,several major sand sources in the northeast region,the northern plain region and the southeast region of the study area were introduced into the analysis field.According to the spatial distribution of aerosol components in the assimilation experiment,the large value area of atmospheric aerosol in the desert Gobi and Beijing area is mainly due to the increase of dust aerosol,and the AOD over South China is mainly composed of P25 component.Assimilation experiments all reflect the increment center of AOD in the study area and the distribution is consistent with the high value area of AOD in the satellite observation field,and the aerosol information in the analysis field is more abundant after the adjustment of assimilation.The experimental results show that the application of FY-4A satellite aerosol data assimilation system in air quality prediction has a broad development prospect.(4)In order to combine the advantages of the data of Fengyun-4 and Himawari-8 satellites,an assimilation system which can simultaneously assimilate the aerosol data from the two satellites was constructed.The results show that the simultaneous assimilation experiment from FY-4A and Himawari-8 aerosol data can make full use of the advantages of the data coverage of the two satellites in different regions,the increase of observational data enriches the AOD distribution area in Beijing,Inner Mongolia and Northeast China,especially in Northwest China,and provide more abundant aerosol observation information and more accurate description of the initial field of the model for the analysis field.