Abstract
The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300–500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km × 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, AODGEMS = 1.18AODOMAERUV + 0.09. An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not significantly improved, the simultaneous consistent retrieval of AOD, SSA and ALH alone demonstrates the value of this stand-alone algorithm, given their nature for error using other methods. The use of these properties as input parameters for the air mass factor calculation is expected to improve the retrieval of other trace gases over Asia.
Highlights
Rapid industrialization and urbanization in Asia has raised serious concerns related to air quality and climate change
This study develops a UV-visible algorithm to retrieve aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) from Geostationary Environment Monitoring Spectrometer (GEMS) measurements
The operational products of the Ozone Monitoring Instrument (OMI) UV algorithm and MODerate resolution Imaging Spectrophotometer (MODIS) RGB image are shown in the figure
Summary
Rapid industrialization and urbanization in Asia has raised serious concerns related to air quality and climate change. Aerosol properties can be measured by ground-based instruments locally and their regional-scale variations can be observed using satellite remote sensing. Over Asia, serial geostationary satellite projects, such as the Geostationary Korea Multi-Purpose Satellite (Geo-KOMPSAT), the Feng-Yun (FY) and the Himawari have been operated by the Korean, Chinese and Japanese governments, respectively and several algorithms to retrieve aerosol information have been developed optimized to each instrument. Since the Geo-KOMPSAT-1 was launched in 2010, its Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI) have been used to monitor aerosol properties over northeast Asia. The MI provides a single visible measurement of AOD with 4 km × 4 km resolution on a 15 min interval [2] and a multi-visible/near-IR GOCI algorithm retrieves AOD, fine-mode fraction (FMF) and aerosol type with 6 km × 6 km resolution on a 1 h interval [3,4,5]. The Advanced Himawari Imager (AHI) was launched in 2013 and provides AOD and the Ångström exponent from multi-visible and near-IR measurements [6]
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