The purpose of this paper is to quantify the sensitivity

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

It has been almost four decades since the first launch of geostationary meteorological satellite by Japan Meteorological Agency (JMA). The specifications of the geostationary meteorological satellites have shown tremendous progresses along with the generations, which are now entering their third generation. The third-generation geostationary meteorological satellites not only yield basic data for weather monitoring, but also globally observe the Earth's environment. The development of multi-band imagers with improved spatial resolution onboard the thirdgeneration geostationary meteorological satellites brings us meteorological data in larger size than those of the secondgeneration ones. Thus, new techniques for domestic and world-wide dissemination of the observational big data are Department of Medical Informatics, Ehime University, Ehime, Japan 7 CLEALINKTECHNOLOGY Co., Ltd., Kyoto, Japan needed. In this paper, we develop a web-based data visualization for Himawari-8 satellite sensed images in real time and with full resolution. This data visualization is supported by the ecosystems, which uses a tiled pyramid representation and parallel processing technique for terrain on an academic cloud system. We evaluate the performance of our techniques for domestic and international users on laboratory experiments. The results show that our data visualization is suitable for practical use on a temporal preview of observation image data for the domestic users.

Cloud classification is not only important for weather forecasts, but also for radiation budget studies. Although cloud mask and classification procedures have been proposed for Himawari-8 Advanced Himawari Imager (AHI), their applicability is still limited to daytime imagery. The split window algorithm (SWA), which is a mature algorithm that has long been exploited in the cloud analysis of satellite images, is based on the scatter diagram between the brightness temperature (BT) and BT difference (BTD). The purpose of this research is to examine the usefulness of the SWA for the cloud classification of both daytime and nighttime images from AHI. We apply SWA also to the image data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra to highlight the capability of AHI. We implement the cloud analysis around Japan by employing band 3 (0.469 μm) of MODIS and band 1 (0.47 μm) of AHI for extracting the cloud-covered regions in daytime. In the nighttime case, the bands that are centered at 3.9, 11, 12, and 13 µm are utilized for both MODIS and Himawari-8, with somewhat different combinations for land and sea areas. Thus, different thresholds are used for analyzing summer and winter images. Optimum values for BT and BTD thresholds are determined for the band pairs of band 31 (11.03 µm) and 32 (12.02 µm) of MODIS (SWA31-32) and band 13 (10.4 µm) and 15 (12.4 µm) of AHI (SWA13-15) in the implementation of SWA. The resulting cloud mask and classification are verified while using MODIS standard product (MYD35) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. It is found that MODIS and AHI results both capture the essential characteristics of clouds reasonably well in spite of the relatively simple scheme of SWA based on four threshold values, although a broader spread of BTD obtained with Himawari-8 AHI (SWA13-15) could possibly lead to more consistent results for cloud-type classification than SWA31-32 based on the MODIS sensors.

No abstract