Applying the Dark Target aerosol algorithm with Advanced Himawari Imager observations during the KORUS-AQ field campaign

Abstract

Abstract. For nearly 2 decades we have been quantitatively observing the Earth's aerosol system from space at one or two times of the day by applying the Dark Target family of algorithms to polar-orbiting satellite sensors, particularly MODIS and VIIRS. With the launch of the Advanced Himawari Imager (AHI) and the Advanced Baseline Imagers (ABIs) into geosynchronous orbits, we have the new ability to expand temporal coverage of the traditional aerosol optical depth (AOD) to resolve the diurnal signature of aerosol loading during daylight hours. The Korean–United States Air Quality (KORUS-AQ) campaign taking place in and around the Korean peninsula during May–June 2016 initiated a special processing of full-disk AHI observations that allowed us to make a preliminary adoption of Dark Target aerosol algorithms to the wavelengths and resolutions of AHI. Here, we describe the adaptation and show retrieval results from AHI for this 2-month period. The AHI-retrieved AOD is collocated in time and space with existing AErosol RObotic NETwork stations across Asia and with collocated Terra and Aqua MODIS retrievals. The new AHI AOD product matches AERONET, and the standard MODIS product does as well, and the agreement between AHI and MODIS retrieved AOD is excellent, as can be expected by maintaining consistency in algorithm architecture and most algorithm assumptions. Furthermore, we show that the new product approximates the AERONET-observed diurnal signature. Examining the diurnal patterns of the new AHI AOD product we find specific areas over land where the diurnal signal is spatially cohesive. For example, in Bangladesh the AOD increases by 0.50 from morning to evening, and in northeast China the AOD decreases by 0.25. However, over open ocean the observed diurnal cycle is driven by two artifacts, one associated with solar zenith angles greater than 70∘ that may be caused by a radiative transfer model that does not properly represent the spherical Earth and the other artifact associated with the fringes of the 40∘ glint angle mask. This opportunity during KORUS-AQ provides encouragement to move towards an operational Dark Target algorithm for AHI. Future work will need to re-examine masking including snow mask, re-evaluate assumed aerosol models for geosynchronous geometry, address the artifacts over the ocean, and investigate size parameter retrieval from the over-ocean algorithm.

Integration of GOCI and AHI Yonsei Aerosol Optical Depth Products During the 2016 KORUS-AQ and 2018 EMeRGe Campaigns

“…Furthermore, AOD retrieval over vegetated areas is more accurate with the ESR method. This result is consistent with previous studies of aerosol retrieval in the VIS region (Levy et al, 2013;Gupta et al, 2019;Hsu et al, 2019).…”

“…CC BY 4.0 License. al., 2005), Meteosat series in Europe (Bernard et al, 2011), Himawari series in Japan (Yoon et al, 2007;Kim et al, 2008;Lim et al, 2018;Kikuchi et al, 2018;Yoshida et al, 2018;Gupta et al, 2019), and the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT, GK) series in South Korea (Kim et al, 2014(Kim et al, , 2016Choi et al, 2016Choi et al, , 2018Kim et al, 2020). However, previously launched geostationary meteorological satellites had only a single, broadband VIS channel, with which it is difficult to retrieve AOPs other than aerosol optical depth (AOD) (Wang et al, 2003;Knapp et al, 2005;Kim et al, 2008Kim et al, , 2014Kim et al, , 2016Bernard et al, 2011).…”

“…However, the Geostationary Ocean Color Imager (GOCI) onboard the GK-1 satellite, also known as the Communication, Ocean, and Meteorological Satellite (COMS), has six VIS and two near-infrared (NIR) channels, which is advantageous for retrieving AOPs (Lee et al, 2010;Choi et al, 2016Choi et al, , 2018Kim et al, 2017). Nextgeneration meteorological GEO satellite instruments, including the Advanced Himawari Imager (AHI), Advanced Baseline Imager (ABI), and Advanced Meteorological Imager (AMI), have three to four VIS and NIR channels, which enable aerosol property retrieval with high accuracy (Lim et al, 2016(Lim et al, , 2018Kikuchi et al, 2018;Yoshida et al, 2018;Gupta et al, 2019). Kikuchi et al (2018) and Yoshida et al (2018) performed aerosol retrievals using the MRM and corrected reflectance using empirical equations.…”

“…Kikuchi et al (2018) and Yoshida et al (2018) performed aerosol retrievals using the MRM and corrected reflectance using empirical equations. Gupta et al (2019) extended the MODIS DT algorithm to GEO satellites and estimated visible surface reflectance using SWIR reflectance. Lim et al (2018) retrieved the AOPs using both MRM and estimated surface reflectance from short-wave IR (SWIR) data (estimated surface reflectance, ESR), and presented the two merged products: an L2-AOD merged product, and a reprocessed AOD produced by merging MRM and ESR surface reflectances.…”

Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

“…In addition, aerosols impact air quality and thus affect human health (e.g., Pope and Dockery 2006). Satellite-retrieved aerosol optical depth (AOD), a quantitative measure of the amount of aerosols present in the atmosphere, is useful for evaluating aerosols' effect on climate change (e.g., Yu et al, 2006) and for estimating and forecasting ambient PM 2.5 concentrations (particulate matter with median diameter ≤ 2.5 µm; e.g., Hoff and Christopher, 2009).…”