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Abstract
Geostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signals received from the surface and the atmosphere, the accurate retrieval of multiple aerosol parameters over land is difficult. With the strategy of taking full advantage of satellite measurement information, here we propose a neural network AEROsol retrieval framework for geostationary satellite (NNAeroG), which can potentially be applied to different instruments to obtain various aerosol parameters. NNAeroG was applied to the Advanced Himawari Imager on Himawari-8 and the results were evaluated versus independent ground-based sun photometer reference data. The aerosol optical depth, Ångström exponent and fine mode fraction produced by the NNAeroG method are significantly better than the official JAXA aerosol products. With spectral bands selection, the use of thermal infrared bands is meaningful for aerosol retrieval.
Highlights
Publisher’s Note: MDPI stays neutralAtmospheric aerosols exert key influences on global climate and the environment [1].Measurements using ground-based instruments can provide a multitude of aerosol parameters, which together characterize the aerosol microphysical and chemical properties in great detail and with high accuracy
The NNAeroG algorithm framework is proposed for aerosol retrieval using data from geostationary satellites
The NNAeroG was applied to Himawari-8/Advanced Himawari Imager (AHI) data to produce aerosol optical depth (AOD), AE and fine mode fraction (FMF) retrievals with 2 km spatial resolution and 10 min temporal resolution
Summary
Atmospheric aerosols exert key influences on global climate and the environment [1]. Measurements using ground-based instruments can provide a multitude of aerosol parameters, which together characterize the aerosol microphysical and chemical properties in great detail and with high accuracy. The use of space-borne radiometers to obtain aerosol information from the radiances or reflectances measured at the top of the atmosphere (TOA, i.e., the dividing line between the Earth’s atmosphere and space) [1,2] requires the development of retrieval methods based on radiative transfer models. Ge et al (2018) [13] proposed a DT method for Himawari-8/AHI aerosol retrieval by defining a new normalized difference vegetation index (NDVI) calculated from the 0.86 μm and 2.3 μm wavebands; the retrieved aerosol optical depth (AOD) possessed an R2 of 0.81 with ground-based network measurements. For Himawari-8/AHI, She et al (2020) [19] trained a deep neural network by AERONET observations to retrieve AOD using reflectances in. A neural network AEROsol retrieval algorithm for geostationary satellite (NNAeroG) is presented. The multiple aerosol products retrieved by NNAeroG can be used in air quality monitoring and climate research
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