The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.

A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.

Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia–Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016 to 2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy, and high-resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher, but the product accuracy is also higher than that of those products. In particular, hourly and daily root-mean-square errors of the new SWDR are 104.9 and 31.5 W m⁻², respectively, which are much smaller than those of CERES (at 121.6 and 38.6 W m⁻², respectively), ERA5 (at 176.6 and 39.5 W m⁻², respectively), and GLASS (daily of 36.5 W m⁻²). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 W m⁻², respectively, which are comparable to that of CERES and ERA5, and even better over high-altitude regions.

Cloud detection by passive satellite sensors is very challenging in hazy weather over China because the reflective characteristics of haze and clouds are very similar. Consequently, hazy areas tend to be mistaken as cloudy or clear areas by current cloud mask algorithms. The Advanced Himawari Imager (AHI) aboard Himawari‐8 is a multispectral Earth observation sensor with high temporal and spatial resolutions. A cloud and haze detection algorithm for AHI measurements is urgently needed for monitoring atmospheric pollution and its transport over China. This study presents the new Himawari‐8 Cloud and Haze Mask (HCHM) algorithm that classifies image pixels from central and eastern China into one of three categories: clear, cloudy, or hazy. Based on the observations that haze occurs near the ground and accumulates in low‐elevation plains and basins while clouds form at high altitudes, the proposed HCHM algorithm incorporates altitude information to adjust the thresholds used in the selected threshold tests to separate haze and cloud pixels. We find that combining auxiliary digital elevation model data with traditional indicators, such as the R_0.86/R_0.64, R_0.86/R_1.6, and BT₁₁‐BT_3.9, improves the accuracy of cloud and haze discrimination. The HCHM algorithm is applied to Himawari‐8 observations from August 2015, November 2015, January 2016, and May 2016 and validated against the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation vertical feature mask results. The validation shows that the average leakage rate, false alarm rate, and haze missing rate of the HCHM algorithm are 3.95%, 5.88%, and 4.17%, respectively.

In this study, various ice particle habits are investigated in conjunction with inferring the optical properties of ice clouds for use in the Global Change Observation Mission-Climate (GCOM-C) satellite programme. We develop a database of the single-scattering properties of five ice habit models: plates, columns, droxtals, bullet rosettes, and Voronoi. The database is based on the specification of the Second Generation Global Imager (SGLI) sensor on board the GCOM-C satellite, which is scheduled to be launched in 2017 by the Japan Aerospace Exploration Agency. A combination of the finite-difference time-domain method, the geometric optics integral equation technique, and the geometric optics method is applied to compute the single-scattering properties of the selected ice particle habits at 36 wavelengths, from the visible to the infrared spectral regions. This covers the SGLI channels for the size parameter, which is defined as a single-particle radius of an equivalent volume sphere, ranging between 6 and 9000 µm. The database includes the extinction efficiency, absorption efficiency, average geometrical cross section, single-scattering albedo, asymmetry factor, size parameter of a volume-equivalent sphere, maximum distance from the centre of mass, particle volume, and six nonzero elements of the scattering phase matrix. The characteristics of calculated extinction efficiency, single-scattering albedo, and asymmetry factor of the five ice particle habits are compared. Furthermore, size-integrated bulk scattering properties for the five ice particle habit models are calculated from the single-scattering database and microphysical data. Using the five ice particle habit models, the optical thickness and spherical albedo of ice clouds are retrieved from the Polarization and Directionality of the Earth's Reflectances-3 (POLDER-3) measurements, recorded on board the Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar (PARASOL) satellite. The optimal ice particle habit for retrieving the SGLI ice cloud properties is investigated by adopting the spherical albedo difference (SAD) method. It is found that the SAD is distributed stably due to the scattering angle increases for bullet rosettes with an effective diameter (D_eff) of 10 µm and Voronoi particles with D_eff values of 10, 60, and 100 µm. It is confirmed that the SAD of small bullet-rosette particles and all sizes of Voronoi particles has a low angular dependence, indicating that a combination of the bullet-rosette and Voronoi models is sufficient for retrieval of the ice cloud's spherical albedo and optical thickness as effective habit models for the SGLI sensor. Finally, SAD analysis based on the Voronoi habit model with moderate particle size (D_eff = 60 µm) is compared with the conventional general habit mixture model, inhomogeneous hexagonal monocrystal model, five-plate aggregate model, and ensemble ice particle model. The Voronoi habit model is found to have an effect similar to that found in some conventional models for the retrieval of ice cloud properties from space-borne radiometric observations.