Comparison of Cloud Type Classification with Split Window Algorithm Based on Different Infrared Band Combinations of Himawari-8 Satellite

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

Comparison of Aqua/Terra MODIS and Himawari-8 Satellite Data on Cloud Mask and Cloud Type Classification Using Split Window Algorithm

“…The cloud classification is implemented with the SWA while using BT31 and BTD31-32 for MODIS data and BT13 and BTD13-15 for Himawari-8 AHI data within the cloud mask area [24]. Figure 4a shows the matrix of SWA, which has nine regions that represent different cloud types.…”

“…Second, we propose that the cloud masking process can be applied to both daytime and nighttime data of AHI and MODIS. In this regard, this work is an extension of our previous study [24], in which cloud masking was only discussed for the daytime data of AHI. We employ the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data to validate the present results [35,36], as in our previous case [24].…”

“…In this regard, this work is an extension of our previous study [24], in which cloud masking was only discussed for the daytime data of AHI. We employ the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data to validate the present results [35,36], as in our previous case [24]. Additionally, the MODIS standard product (MYD35) [5,31] is exploited to check the probability of detection (POD).…”

“…Hamada et al [23] further modified the SWA by using millimeter-wave radar and GM-5 data. More recently, Purbantoro et al [24] applied SWA to the Himawari-8 AHI data. They tested the band combinations of band [13][14][15], and band [15][16], and concluded that, generally, SWA13-15 can detect more cloud types than SWA15-16, though the latter is more sensitive to rain-band clouds such as cumulonimbus.…”

Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS

“…Several methods have been developed to classify clouds from single-or multispectral satellite imageries, including threshold-based [12][13][14][15][16] and machine learning approaches. The main drawback of threshold-based cloud-type classification (e.g., Reference [17]) is that a threshold over certain situations may not be applicable for another [18]. Also, a large number of studies have addressed satellite cloud-type classification from a variety of perspectives but they rely on specific regions.…”