Evaluation and uncertainty estimate of next-generation geostationary meteorological Himawari-8/AHI aerosol products

The next-generation geostationary meteorological Himawari-8 satellite carrying the Advanced Himawari Imager (AHI) allows frequent observations of the atmosphere, the surface, and oceans every 10 min. With its retrieval algorithms recently updated, Himawari-8/AHI Version 2 Level 2 aerosol products are now available. However, these retrievals have not yet undergone a quality assessment. This study aims to comprehensively validate the official aerosol optical properties derived from Himawari-8/AHI over land and ocean. Aerosol Robotic Network and Sun-Sky Radiometer Observation Network ground-based measurements at 98 stations in the Himawari-domain region are used to validate aerosol optical depth (AOD, or tau) retrievals at 500 nm and Angstrom exponent (AE) retrievals at 440-675 nm from the year 2016. The AOD retrievals agree well with surface observations (i.e., from linear regression, slope = 0.876, intercept = 0.076, and correlation coefficient = 0.756) with a mean absolute error and a root-mean-square error of 0.168 and 0.293, respectively. On site and regional scales, large uncertainties are seen, especially in Australia (significant overestimation) and South Asia (significant underestimation). The AOD retrievals can correctly capture daily variations and show the best (worst) performance in summer (spring). The AE performance is poorer on all scales, showing overall underestimations, especially in Australia, Southeast Asia, and China. The data quality of AOD retrievals improves as the vegetation coverage and the AE increases. This suggests that the official aerosol retrieval algorithm still faces great challenges over bright surfaces and under coarse-particle-dominated conditions. In general, approximately 61% and 64% of the AOD matchups meet the newly defined expected errors of [0.330 x tau + 0.024; -0.132 x tau - 0.125] and [0.519 x tau + 0.005; -0.007 x tau - 0.194] determined by ground measurements and aerosol retrievals, respectively. The highly variable accuracy of aerosol retrievals raises a concern about the reliability of the current product under different environmental conditions and underlying surfaces. It also sheds light on what future improvements need implementing to the aerosol retrieval algorithm. (C) 2019 Elsevier B.V. All rights reserved.