This paper presents a new algorithm to retrieve the aerosol optical depth (AOD) from a Himawari-8 Advanced Himawari Imager (AHI). Six typical aerosol models that derived from the long-term ground-based observations of East Asia are used in AOD retrieval. To accurately determine the surface reflectance, improved channel relationships between red, blue, and shortwave infrared (SWIR) are built up according to the infrared Normalized Difference Vegetation Index (NDVI_SWIR). Based on the new derived aerosol models and improved channel relationships, AOD over East Asian is retrieved by using the AHI data. The results are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MOD04 and MYD04) and yielded a correlation coefficient lager than 0.8 (R = 0.87 and 0.92, respectively). In addition, the retrieved AOD values are also validated by ground-based measurements at 12 Aerosol Robotic Network (AERONET) locations and revealed a good agreement between them (R = 0.86).

Several essential improvements have been made in recent Ozone Monitoring Instrument (OMI) near‐ultraviolet (UV) aerosol retrieval algorithm version (OMAERUV version 1.4.2), but few regional validations for its aerosol optical depth (AOD) product are conducted. This paper assessed the OMAERUV AOD product over Central and East Asia. The OMAERUV Level 2.0 AOD product was compared with Aerosol Robotic Network (AERONET) Level 2.0 direct Sun AOD measurement over 10 years (2005–2014) at 27 selected AERONET sites. A combined comparison of OMAERUV‐AERONET AOD at 25 (2) sites was carried out and yielded correlation coefficient (ρ) of 0.63 (0.77), slope of 0.53 (0.57), y intercept of 0.18 (0.13), and 50.71% (57.24%) OMAERUV AOD fall within the expected uncertainty boundary (larger by 0.1 or ±30%) at 380 nm (440 nm). The more accurate (ρ > 0.70) OMAERUV retrievals are reported over eastern and northern China and South Korea. The two primary reasons for the underestimation of OMAERUV AOD over China are as follows: (1) the use of single‐channel (388 nm) retrieval method retrieves scattering AOD and not total AOD, and (2) the spectral dependence of the imaginary part of the refractive index in the near‐UV region assumed in the algorithm may not be representative of aerosols found over China. The comparisons for three predominant aerosol types indicate that smoke aerosol exhibits the best performance, followed by dust and nonabsorbing aerosol. It is consistent with the characteristic of near‐UV wavelength that it is more sensitive to absorbent particles. The comprehensive yearly (2005–2014) comparison at 25 sites and comparison between two periods (2005–2006 and 2009–2014) at selected four sites show no discernible decrease of temporal trend, which indicates that the OMAERUV algorithm successfully maintains its quality of aerosol product despite post‐2008 row anomaly instrument problem.

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.

The launch of the National Oceanic and Atmospheric Administration (NOAA)/ National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and its follow-on NOAA Joint Polar Satellite Systems (JPSS) satellites marks the beginning of a new era of operational satellite observations of the Earth and atmosphere for environmental applications with high spatial resolution and sampling rate. The S-NPP and JPSS are equipped with five instruments, each with advanced design in Earth sampling, including the Advanced Technology Microwave Sounder (ATMS), the Cross-track Infrared Sounder (CrIS), the Ozone Mapping and Profiler Suite (OMPS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Clouds and the Earth’s Radiant Energy System (CERES). Among them, the ATMS is the new generation of microwave sounder measuring temperature profiles from the surface to the upper stratosphere and moisture profiles from the surface to the upper troposphere, while CrIS is the first of a series of advanced operational hyperspectral sounders providing more accurate atmospheric and moisture sounding observations with higher vertical resolution for weather and climate applications. The OMPS instrument measures solar backscattered ultraviolet to provide information on the concentrations of ozone in the Earth’s atmosphere, and VIIRS provides global observations of a variety of essential environmental variables over the land, atmosphere, cryosphere, and ocean with visible and infrared imagery. The CERES instrument measures the solar energy reflected by the Earth, the longwave radiative emission from the Earth, and the role of cloud processes in the Earth’s energy balance. Presently, observations from several instruments on S-NPP and JPSS-1 (re-named NOAA-20 after launch) provide near real-time monitoring of the environmental changes and improve weather forecasting by assimilation into numerical weather prediction models. Envisioning the need for consistencies in satellite retrievals, improving climate reanalyses, development of climate data records, and improving numerical weather forecasting, the NOAA/Center for Satellite Applications and Research (STAR) has been reprocessing the S-NPP observations for ATMS, CrIS, OMPS, and VIIRS through their life cycle. This article provides a summary of the instrument observing principles, data characteristics, reprocessing approaches, calibration algorithms, and validation results of the reprocessed sensor data records. The reprocessing generated consistent Level-1 sensor data records using unified and consistent calibration algorithms for each instrument that removed artificial jumps in data owing to operational changes, instrument anomalies, contaminations by anomaly views of the environment or spacecraft, and other causes. The reprocessed sensor data records were compared with and validated against other observations for a consistency check whenever such data were available. The reprocessed data will be archived in the NOAA data center with the same format as the operational data and technical support for data requests. Such a reprocessing is expected to improve the efficiency of the use of the S-NPP and JPSS satellite data and the accuracy of the observed essential environmental variables through either consistent satellite retrievals or use of the reprocessed data in numerical data assimilations.