Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 µm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 µm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination

The understanding of aerosol properties in troposphere, especially their behavior near the ground level, is indispensable for precise evaluation of their impact on the Earth's radiation studies. Although a sunphotometer or a skyradiometer can provide the aerosol optical thickness (AOT), their application is limited to daytime under near cloud free conditions. In order to attain the multi-wavelength observation for both day-and night-time including cloudy conditions, here we propose a novel monitoring technique by means of simultaneous measurement using a nephelometer (450, 550, and 700 nm), an aethalometer (370, 470, 520, 590, 660, 880, and 950 nm), and a visibility meter (550 nm). On the basis of the multi-wavelength data of scattering and absorption coefficients from the nephelometer and aethalometer, respectively, first we calculate the real-time values of aerosol extinction coefficient in addition to the Angstrom exponent (AE). Then, correction of these values is carried out by comparing the resulting extinction coefficient with the corresponding value obtained from the optical data of visibility-meter. The major reason for this correction is the loss of relatively coarse particles due to the aerodynamic effect as well as evaporation of water content from particles during the sampling procedure. Then, with the ancillary data of vertical aerosol profile obtained with a lidar (532 nm), the temporal change of AOT is estimated. In this way, information from the sampling can be converted to the ambient properties in the atmospheric boundary layer. Furthermore, daytime data from a

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Observation of optical properties of atmospheric aerosols, especially their behavior near the surface level, is indispensable for better understanding of atmospheric environmental conditions. Concurrent observations of ground-based instruments and satellite-borne sensors are useful for attaining improved accuracy in the observation of relatively wide area. In the present paper, aerosol parameters in the lower troposphere are monitored using a plan position indicator (PPI) lidar, ground-sampling instruments (a nephelometer, an aethalometer, and optical particle counters), as well as a sunphotometer. The purpose of these observations is to retrieve the aerosol extinction coefficient (AEC) and aerosol optical thickness (AOT) simultaneously at the overpass time of Landsat-8 satellite. The PPI lidar, operated at 349 nm, provides nearly horizontal distribution of AEC in the lower part of the atmospheric boundary layer. For solving the lidar equation, the boundary condition and lidar ratio are determined from the data of ground sampling instruments. The value of AOT, on the other hand, is derived from sunphotometer, and used to analyze the visible band imagery of Landsat-8 satellite. The radiative transfer calculation is conducted using the MODTRAN code with the original aerosol type that has been determined from the ground sampling data coupled with the Mie scattering calculation. Reasonable agreement is found between the spatial distribution of AEC from the PPI lidar and that of AOT from the blue band (band 2) of Landsat-8. The influence of AOT on the values of apparent surface reflectance is also discussed.

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