GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

Abstract

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements made to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm together with validation results during the Distributed Regional Aerosol Gridded Observation Networks -Northeast Asia 2012 campaign (DRAGON-NE Asia 2012 campaign). The evaluation during the spring season over East Asia is important because of high aerosol concentrations and diverse types of Asian dust and haze. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, Ångström exponent (AE) between 440 and 860 nm, and aerosol type. The aerosol models are created based on a global analysis of the Aerosol Robotic Networks (AERONET) inversion data, and covers a broad range of size distribution and absorptivity, including nonspherical dust properties. The Cox-Munk ocean bidirectional reflectance distribution function (BRDF) model is used over ocean, and an improved minimum reflectance technique is used over land. Because turbid water is persistent over the Yellow Sea, the land algorithm is used for such cases. The aerosol products are evaluated against AERONET observations and MODIS Collection 6 aerosol products retrieved from Dark Target (DT) and Deep Blue (DB) algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson correlation coefficient of 0.881 and a linear regression equation with GOCI AOD = 1.083 × AERONET AOD − 0.042. The correlation between GOCI and MODIS AODs is higher over ocean than land. GOCI AOD shows better agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE, FMF, and SSA) show lower correlation with AERONET than AOD, but still show some skills for qualitative use.

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

“…Note that the GOCI AE is derived from the predefined values of the selected aerosol model, not from the retrieved spectral AOD. Compared with the V1 AE accuracy during the DRAGON-NE Asia 2012 campaign described by Choi et al (2016; R = 0.678 over both land and ocean surfaces), the V2 land and ocean AE have lower linear correlations with AERONET (R = 0.505 and 0.459, respectively) from the 5-year validation. The DRAGON-NE Asia 2012 campaign was conducted in spring (March-May) when long-range transport of yellow dust from the Gobi and Taklamakan deserts in Asia, which has low AE with high AOD, is more frequent.…”

“…The TOA reflectance of the remaining pixels is averaged if the number of remaining pixels is greater than 72 (Step 9 in Table 1). In this step, the darkest 20 % and brightest 40 % of pixels are discarded to filter out cloud shadow, remaining cloud, and surface contamination, following Choi et al (2016). The quality assurance (QA) value of the V1 algorithm was determined based on the range of retrieved AOD and the remaining number of pixels in a 12 pixel × 12 pixel block after all masking procedures were performed.…”

“…Land surface reflectance is obtained using the minimum reflectivity technique for each month, channel, and hour, and temporal interpolation is carried out over land, turbid ocean, and heavy aerosol pixels in the inversion step. In the algorithm, turbid water pixel detection is implemented using a difference of 660 nm TOA reflectance between directly observed and interpolated data from 412 and 865 nm (hereafter, ρ 660 ; Li et al, 2003;Choi et al, 2016).…”

“…However, this methodology prevents the GOCI YAER V1 algorithm from being capable of near-real-time (NRT) processing because it required a monthly database for each year encompassing the day of retrieval for the determination of surface reflectance. In addition, the resulting retrievals have a slightly negative bias over land and a positive bias over ocean due to surface reflectance errors, compared with AERONET data during the Distributed Regional Aerosol Gridded Observation Networks -Northeast Asia 2012 campaign (DRAGON-NE Asia 2012 campaign; Choi et al, 2016).…”

“…Using the radiance measurements from eight spectral channels (412,443,490,555,660,680,745, and 865 nm) with high spatial resolution (500 m × 500 m), the GOCI Yonsei aerosol retrieval (YAER) version 1 (V1) algorithm was developed to retrieve hourly aerosol optical properties such as aerosol optical depth (AOD) with simple diagnostic parameters such as fine-mode fraction (FMF), Ångström exponent (AE), and single-scattering albedo (SSA; Choi et al, 2016). Because it has more channels with higher spatial resolution in the visible and near-infrared (NIR) bands compared with recent and planned advanced meteorological sensors in GEO, including the Advanced Himawari Imager (AHI), the Advanced Baseline Imager (ABI), and the Advanced Meteorological Imager (AMI), GOCI provides valuable information related to AOPs.…”