Retrieval And Application Of Absorbing Aerosol Index Based On Satellite Remote Sensing

Atmospheric aerosol is a complex system composed of solid and liquid particles suspended in the atmosphere,and is one of the main atmospheric components affecting regional and global climate change,atmospheric environmental quality and human health.The presence of absorbing aerosols affects the radiative balance and the radiative flux reaching the ground.The absorbing aerosol index(AAI)is an optical parameter indicating the existence of absorbing aerosols.It can qualitatively monitor the spatial distribution and transmission process of absorbing aerosols,and is widely used in research effects of absorbing aerosols on Earth’s radiation,climate,as well as monitoring major pollution events such as dust storms,biomass burning,and volcanic eruptions.The calculation of AAI is relatively simple,insensitive to surface types,and AAI can be inverted under cloudy conditions,In view of the above advantages,AAI has become one of the important indicators for satellite monitoring of absorbing aerosols.First,we create a multidimensional lookup table of input parameters by using the SCIATRAN radiative transfer model.Based on the first-level data of Tropospheric Monitoring Instrument(TROPOMI),we use the ultraviolet spectral comparison method to retrieve the AAI,and obtain the initial inversion results including instrument calibration errors and the impact of viewing geometries.Based on this,the paper proposes a statistical method of background value to correct AAI.In the process of calculation,the observation data of the two loads of TROPOMI AAI and MODIS AOD are combined to ensure that the distribution of aerosols are uniform and not affected by absorbing aerosols in the selected area.We can obtain the spatial distribution of background value by combining with TROPOMI cloud products and a geometric criterion to filter the cloudy pixels.At the same time,we should select the appropriate days.We also need to remove the pixels,which satisfy the sun glint condition.From the spatial distribution characteristics of the background value,it is known that the background value is a function of time,latitude and ground pixels,and itself includes the calibration error of the instrument and the correlation of the viewing geometries.The spatial and temporal distribution characteristics of background values were then further analyzed using TROPOMI observation data in 2019,2020,and 2021.In this paper,the application of the background value to the initial inversion results of TROPOMI AAI have efficiently reduced the influence of instrument calibration errors and viewing geometries on AAI.The spatial distribution of AAI after background value correction are compared with with the operational TROPOMI official UVAI data product,which have better consistency and correlation.The coefficient is 0.95.The background value also solve the AAI inversion offset problem caused by the TROPOMI instrument degradation after 1 July 2021,and has better universality.In addition,we also provided a detailed analysis of an extreme wildfire event,which delivered large black carbon loads in California,USA in September 2020,which can clearly show the distribution and transmission of absorbing aerosol smoke plumes in the atmosphere.The AOD observed from Aerosol Robotic Network were further used in conjunction with AAI to give more information about the temporal distribution of absorbing aerosols.Furthermore,the reflectances between reflectivity and AAI was simulated using a radiation transfer model,and it was found that AAI is very sensitive to reflectances,so the background value of AAI can also be used as a sensitivity indicator to monitor changes in the state of the TROPOMI instrument.Finally,we realize the retrieval of AAI and obtain the global distribution of AAI based on the first domestic atmospheric trace gas monitoring instrument EMI.First,we improve the accuracy of EMI spectral data by evaluating radiation data and reflectances correction.To effectively remove stripe noise in EMI AAI data products by using Fourier transform method.The real-time correction of background value for AAI with instrument calibration errors can reduce the bias of the AAI results.In the four regions of Sahara Desert,Taklamakan Desert,Southern Africa and Middle East,the correlation between monthly average EMI and TROPOMI AAI reached 0.92,0.93,0.92 and 0.94,respectively.The EMI AAI and TROPOMI AOD have a good consistency.The EMI AAI can monitor the temporary variations of aerosols in August and October 2019 in combination with the ground-based AERONET observation data.We analysis the the spatial and temporal distribution of Australian wildfires based on the EMI AAI,combined with HYSPLIT forward trajectory and CALIPSO aerosol height data.This paper demonstrates the performance and reliability of EMI instrument for monitoring the distribution and transport of absorbing aerosol plumes.The above studies show that the EMI AAI data products are of great significance in monitoring air pollution.