TITLE:
Comparison of Cloud Type Classification with Split Window Algorithm Based on Different Infrared Band Combinations of Himawari-8 Satellite
AUTHORS:
Babag Purbantoro, Jamrud Aminuddin, Naohiro Manago, Koichi Toyoshima, Nofel Lagrosas, Josaphat Tetuko Sri Sumantyo, Hiroaki Kuze
KEYWORDS:
Cloud Type Detection, Himawari-8, Split Window Algorithm, Brightness Temperature
JOURNAL NAME:
Advances in Remote Sensing,
Vol.7 No.3,
September
21,
2018
ABSTRACT: 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 is useful for delineating
cumulonimbus underneath dense cirrus regions