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Measurements with a 763 nm channel, located within the oxygen A-band and equipped on the Second-generation Global Imager (SGLI) onboard the JAXA's Global Change Observation Mission – Climate (GCOM-C) satellite, have the potential to retrieve cloud base height (CBH) and cloud geometric thickness (CGT) through passive remote sensing. This study implemented an algorithm to retrieve the CBH using the SGLI 763 nm channel in combination with several other SGLI channels in the visible, shortwave infrared, and thermal infrared regions. In addition to CBH, the algorithm can simultaneously retrieve other key cloud properties, including cloud optical thickness (COT), cloud effective radius, ice COT fraction as the cloud thermodynamic phase, cloud top height (CTH), and CGT. Moreover, the algorithm can be seamlessly applied to global clouds comprised of liquid, ice, and mixed phases. The SGLI-retrieved CBH exhibited quantitative consistency with CBH data obtained from the ground-based ceilometer network, ship-borne ceilometer, satellite-borne radar and lidar observations, as evidenced by sufficiently high correlations and small biases. These results provide practical evidence that the retrieval of CBH is indeed possible using the SGLI 763 nm channel. Moreover, the results lend credence to the future use of SGLI CBH data, including the estimation of the surface downward longwave radiative flux from clouds. Nevertheless, issues remain that must be addressed to enhance the value of SGLI-derived cloud retrieval products. These include the systematic bias of SGLI CTH related to cirrus clouds and the bias of SGLI CBH caused by multi-layer clouds. [ABSTRACT FROM AUTHOR]

Surface downward solar radiation compositions (SSRC), including photosynthetically active radiation (PAR), ultraviolet-A (UVA), ultraviolet-B (UVB), and shortwave radiation (SWR), with high spatial–temporal resolutions and precision are essential for applications including solar power, vegetation photosynthesis, and environmental health. In this study, an optimal algorithm was developed to calculate SSRC, including their direct and diffuse components. Key features of the algorithm include combining the radiative transfer model with machine learning techniques, including full consideration of the effects of aerosol types, cloud phases, and gas components. A near-real-time monitoring system was developed based on this algorithm, with SSRC products generated from Himawari-8/9 and Fengyun-4 series data. Validation with ground-based data shows that the accuracy of the SWR and PAR compositions (daily mean RMSEs of 19.7 and 9.2 W m−2, respectively) are significantly better than those of state-of-the-art products from CERES, ERA5, and GLASS. The accuracy of UVA and UVB measurements is comparable with CERES. Characteristics of aerosols, clouds, gases, and their impacts on SSRC are investigated before, during, and post COVID-19; in particular, significant SSRC variations due to the reduction of aerosols and increase of ozone are identified in the Chinese central and eastern areas during that period. The spatial–temporal resolution of data products [up to 0.05° (10 min)−1 for the full-disk region] is one of the most important advantages. Data for the East Asia–Pacific region during 2016–20 is available from the CARE home page (www.slrss.cn/care/sp/pc/). [ABSTRACT FROM AUTHOR]

We developed a new aerosol retrieval algorithm combining a numerical aerosol forecast. In the retrieval algorithm, the short-term forecast from an aerosol data assimilation system was used for a priori estimate instead of spatially and temporally constant values. This method was demonstrated using the Advanced Himawari Imager onboard the Japan Meteorological Agency's geostationary satellite Himawari-8, and the results showed spatially finer distributions than the model forecast and less noisy distributions than the original algorithm. We validated the new algorithm using ground observation data and found that the aerosol parameters detectable by satellite sensors were retrieved more accurately than a priori model forecast by adding satellite information. Moreover, the retrieval accuracy was improved by using the model forecast as compared with using constant a priori estimates. By using the assimilated forecast for a priori estimate, information from previous observations can be propagated to future retrievals, thereby leading to better retrieval accuracy. Observational information from the satellite and aerosol transport by the model is incorporated cyclically to effectively estimate the optimum field of aerosol. [ABSTRACT FROM AUTHOR]

High-performance computing resources allow us to conduct numerical simulations with a horizontal grid spacing that is sufficiently high to resolve cloud systems on a global scale, and high-resolution models (HRMs) generally provide better simulation performances than low-resolution models (LRMs). In this study, we execute a next-generation model that is capable of simulating global aerosols on a nonhydrostatic icosahedral atmospheric model version 16 (NICAM.16). The simulated aerosol distributions are obtained for 3 years with a HRM in a global 14-km grid spacing, an unprecedentedly high horizontal resolution and long integration period. For comparison, a NICAM with a 56-km grid spacing is also run as an LRM, although this horizontal resolution is still high among current global climate models. The comparison elucidated that the differences in the various variables of meteorological fields, including the wind speed, precipitation, clouds, radiation fluxes and total aerosols, are generally within 10% of their annual averages, but most of the variables related to aerosols simulated by the HRM are slightly closer to the observations than are those simulated by the LRM. Upon investigating the aerosol components, the differences in the water-insoluble black carbon (WIBC) and sulfate concentrations between the HRM and LRM are large (up to 32%), even in the annual averages. This finding is attributed to the differences in the column burden of the aerosol wet deposition, which is determined by a conversion rate of precipitation to cloud and the difference between the HRM and LRM is approximately 20%. Additionally, the differences in the simulated aerosol concentrations at polluted sites during polluted months between the HRM and LRM are estimated with medians of -23% (-63% to -2.5%) for BC, -4% (-91% to +18%) for sulfate and -1% (-49% to +223%) for the aerosol optical thickness (AOT). These findings indicate that the differences in the secondary and tertiary products, such as the AOT, between the different horizontal grid spacings are not explained simply by the grid size. On a global scale, the subgrid variabilities in the simulated AOT and COT in the 1°×1° domain using 6-hourly data are estimated to be 28.5% and 80.0%, respectively, in the HRM, whereas the corresponding differences are 16.6% and 22.9% in the LRM. Over the Arctic, both the HRM and the LRM generally reproduce the observed aerosols, but the largest difference in the surface BC mass concentrations between the HRM and LRM reaches 30% in spring (the HRM-simulated results are closer to the observations). The vertical distributions of the HRM- and LRM-simulated aerosols are generally close to the measurements, but the differences between the HRM and LRM results are large above a height of approximately 3 km, mainly due to differences in the wet deposition of the aerosols. The global annual averages of the direct and indirect aerosol radiative forcings (ARFs) attributed to anthropogenic aerosols in the HRM are estimated to be -0.29Wm-2 and -0.93Wm-2, respectively, whereas those in the LRM are -0.24Wm-2 and -1.10Wm-2. The differences in the direct ARF between the HRM and LRM are primarily caused by those in the aerosol burden, whereas the differences in the indirect ARF are primarily caused by those in the cloud expression and performance, which are attributed to the grid spacing. Because one-tenth of the computer resources are required for the LRM (56-km grid) compared to the HRM (14-km grid), we recommend that the various tuning parameters associated with the aerosol distributions using the LRM can be applicable to those using the HRM under the limitation of the available computational resources or before the HRM integration. [ABSTRACT FROM AUTHOR]