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A case study of rapid variation (ramp event) of photovoltaic (PV) power generation on 18 April 2016 for the Tokyo Electric Power Company (called “TEPCO”) power area is considered in this study. PV system installation in the TEPCO area has been increasing (approximately 10 GW PV system capacity). Quasi real-time monitoring of PV power generation by using satellite-estimated solar radiation is performed. Under assumptions of future PV system installation scenarios (from 21.78 GW to 85.76 GW in the TEPCO area), temporal and spatial variations of PV power generation are estimated.

Error propagation in Earth’s atmospheric, oceanic, and land surface parameters of the satellite products caused by misclassification of the cloud mask is a critical issue for improving the accuracy of satellite products. Thus, characterizing the accuracy of the cloud mask is important for investigating the influence of the cloud mask on satellite products. In this study, we proposed a method for validating multiwavelength satellite data derived cloud masks using ground-based sky camera (GSC) data. First, a cloud cover algorithm for GSC data has been developed using sky index and bright index. Then, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived cloud masks by two cloud-screening algorithms (i.e., MOD35 and CLAUDIA) were validated using the GSC cloud mask. The results indicate that MOD35 is likely to classify ambiguous pixels as “cloudy,” whereas CLAUDIA is likely to classify them as “clear.” Furthermore, the influence of error propagations caused by misclassification of the MOD35 and CLAUDIA cloud masks on MODIS derived reflectance, brightness temperature, and normalized difference vegetation index (NDVI) in clear and cloudy pixels was investigated using sky camera data. It shows that the influence of the error propagation by the MOD35 cloud mask on the MODIS derived monthly mean reflectance, brightness temperature, and NDVI for clear pixels is significantly smaller than for the CLAUDIA cloud mask; the influence of the error propagation by the CLAUDIA cloud mask on MODIS derived monthly mean cloud products for cloudy pixels is significantly smaller than that by the MOD35 cloud mask.

Computing time and retrieval error of the effective particle radius are important considerations when developing an ice crystal scattering database to be used in radiative transfer simulation and satellite remote sensing retrieval. Therefore, the light scattering database should be optimized based on the specifications of the satellite sensor. In this study, the grid system of the complex refractive index in the 1.6 μm (SW3) channel of the Global Change Observation Mission/Second Generation Global Imager satellite sensor is investigated for optimizing the ice crystal scattering database. This grid system is separated into twelve patterns according to the step size of the real and imaginary parts of the refractive index. Specifically, the LIght Scattering solver Applicable to particles of arbitrary Shape/Geometrical-Optics Approximation technique is used to simulate the scattering of light by randomly oriented large hexagonal ice crystals. The difference of radiance with different step size of the refractive index is calculated from the developed light scattering database using the radiative transfer (R-STAR) solver. The results indicated that the step size of the real part is a significant factor in difference of radiance.

We investigate the cloud detection efficiency of existing and future spaceborne visible-to-infrared imagers, focusing on several threshold tests for cloud detection over different types of ground surfaces, namely, the ocean, desert, vegetation, semibare land, and cryosphere. In this investigation, we used the CLoud and Aerosol Unbiased Decision Intellectual Algorithm (CLAUDIA), which was developed for unbiased cloud detection. It was revealed that imagers with fewer bands than the Moderate Resolution Imaging Spectroradiometer tend to have cloudy shifts. An imager without any infrared bands could yield cloudy shifts up to 17% over the ocean. To avoid false recognition of Sun glint as clouds, the 0.905 and 0.935?μm bands are needed in addition to the infrared bands. In reflectance ratio tests, the 0.87 and 1.6?μm bands can effectively distinguish clouds from desert. In the case of desert, thermal–infrared bands are ineffective when the desert surface temperature is low during winter. The 3.9 and 11?μm bands are critical for distinguishing between clear and cloudy pixels over snow-/ice-covered areas. The results and discussions of this research can guide CLAUDIA users in the optimization of thresholds. Here, we propose a virtual imager called the cloud detection imager, which has seven or eight bands for efficient cloud detection.

We develop a numerical algorithm for calculating the light-scattering properties of small particles of arbitrary shape on the basis of a method involving surface integral equations. The calculation error was estimated by performing a comparison between the proposed method and the exact Mie method with regard to the extinction efficiency factor, and the results show that the error is less than 1%when four or more nodes per wavelength are set on the surface of a spherical particle. The accuracy fluctuates in accordance with the distribution of nodal points on the particle surface with respect to the direction of propagation of the incident light. From our examinations, it is shown that the polar incidence alignment yields higher accuracy than equator incidence when a “latitude-longitude” type of mesh generation is adopted. The electric currents on the particle surface and the phase functions of all scattering directions are shown for particles shaped as spheres or hexagonal columns. It is shown that the phase function for a hexagonal column has four or eight cold spots. The phase function of a randomly oriented hexagonal column shows halolike peaks with size parameters of up to 20. This method can be applied to particles with a size parameter of up to about 20 without using the symmetry characteristic of the particle.

Two new extension modules that give the water-leaving radiance from the ocean and the snow bidirectional reflectance distribution function were implemented in the latest radiative transfer code. In addition, to simulate the near-global distributions of satellite-measured radiances by using the improved radiative transfer code, we tested and applied the look-up table method together with the process-separation technique of the radiative transfer calculation. The computing time was reduced from 1 year to 20 s to simulate one channel, one scene of the Global Imager image by use of an Alpha 21164A-2 (600-MHz) machine. The error analyses showed that the radiances were simulated with less than 1% error for the nonabsorbing visible channels and ∼2% error for absorbing channels by use of this method.

The influence of daylight and noise current on cloud and aerosol observations by realistic spaceborne lidar was examined by computer simulations. The reflected solar radiations, which contaminate the daytime return signals of lidar operations, were strictly and explicitly estimated by accurate radiative transfer calculations. It was found that the model multilayer cirrus clouds and the boundary layer aerosols could be observed during the daytime and the nighttime with only a few laser shots. However, high background noise and noise current make it difficult to observe volcanic aerosols in middle and upper atmospheric layers. Optimal combinations of the laser power and receiver field of view are proposed to compensate for the negative influence that is due to these noises. For the computer simulations, we used a realistic set of lidar parameters similar to the Experimental Lidar in-Space Equipment of the National Space Development Agency of Japan.

The channel specifications of the Global Imager onboard the Advanced Earth Observing Satellite II have been determined by extensive numerical experiments. The results show that there is an optimum feasible position for each ocean color channel. The bandwidth of the 0.763-μm channel should be less than 10 nm for good sensitivity to the cloud top height and geometric thickness of the cloud layer; a 40-nm bandwidth is suitable for the 1.38-μm channel to have the strongest contrast between cloudy and clear radiance with a sufficient radiant energy; and a 3.7-μm channel is better than a 3.95-μm channel for estimation of the sea surface temperature (SST) and determination of the cloud particle size when the bandwidth of the channel is 0.33 μm. A three-wavelength combination of 6.7, 7.3, and 7.5 μm is an optimized choice for water vapor profiling. The combination of 8.6, 10.8, and 12.0 μm is suitable for cloud microphysics and SST retrievals with the split-window technique.

Analyses of solar radiation exchanges between the atmosphere and clouds are vital for the understanding of climate processes and cycles. Comparisons of satellite‐to‐satellite or satellite‐to‐ground‐truth observations aiming at, elucidating the radiative behavior of atmospheric components (clouds, aerosols, gas, etc.), or validating data of a particular satellite are a common practice in global radiation investigations. In order to assess the quality of cloud optical properties derived from Geostationary Meteorological Satellite‐5/Stretched Visible Infrared Spin Scan Radiometer (GMS‐5/SVISSR), the former procedure (satellite‐to‐satellite comparison) was used. Data derived from GMS‐5/SVISSR satellite were compared with those from the polar‐orbiting Terra‐Moderate Resolution Imaging Spectroradiometer (Terra‐MODIS) satellite. This comparison showed serious discrepancies between cloud optical depth (COD) data retrieved from the two satellites' observations. GMS‐5/SVISSR‐retrieved COD appeared mostly lower than that of Terra‐MODIS. To understand the origin of such differences, an identification procedure of the major factors likely to affect these data is conducted. Some of these factors were the satellite viewing and solar conditions, the cloud thermodynamic phase differentiation and particle effective radius, and the cloud inhomogeneity. Then emphasis was put on the examination of the latter effect (i.e., the cloud inhomogeneity). The analysis procedure was as follows: First, data having close‐viewing geometries between both satellites were selected and used to understand the effects of the remaining factors. Among these, the cloud thermodynamic phase appeared to play the major role as analyses showed that most of the COD differences between both satellites were confined within ice clouds while warm clouds had the least discrepancies. This would suggest that the choice of a water cloud particle radiative transfer model to analyze a 2‐phase cloud radiation data, as used here, may produce large uncertainties in ice COD retrievals from at least one of the satellites. To avoid the cloud phase problem, a more restrictive data set comprising only water clouds (besides close‐viewing geometries between both satellites) was selected, and the impact of the degree of cloud inhomogeneity on the COD retrievals was evaluated. The study reveals that the 3‐D radiative effects deriving from the external cloud inhomogeneity, i.e., cloud asymmetry and structured sides, were the most influencing properties here. The GMS‐5/SVISSR interpretation of inhomogeneous cloud optical properties showed larger uncertainties than that of Terra‐MODIS. Furthermore, COD values of GMS‐5/SVISSR were systematically lower than those of Terra‐MODIS for the pixels at shadow sides of the cloud, while at illuminated sides they often showed higher values. For gentle or near‐plane‐parallel cloud surfaces, fewer discrepancies were noticed (the best agreement between both satellites' retrievals). At steep slopes of the shadow and illuminated cloud sides, GMS‐5/SVISSR average COD data were respectively under‐ and overestimated compared to those of Terra‐MODIS. COD differences between the two satellites could be sometimes higher than 30% for slopes steeper than 0.5 K/km.