SST와 CALIPSO 자료를 이용한 DCD 방법으로 정의된 안개화소 분석
Analysis of the Fog Detection Algorithm of DCD Method with SST and CALIPSO Data
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초록
Nighttime sea fog detection from satellite is very hard due to limitation in using visiblechannels. Currently, most widely used method for the detection is the Dual Channel Difference(DCD) method based on Brightness Temperature Difference between 3.7 and 11 μm channel(BTD). However, this method have difficulty in distinguishing between fog and low cloud, andsometimes misjudges middle/high cloud as well as clear scene as fog. Using CALIPSO Lidar Profilemeasurements, we have analyzed the intrinsic problems in detecting nighttime sea fog from varioussatellite remote sensing algorithms and suggested the direction for the improvement of thealgorithm. From the comparison with CALIPSO measurements for May-July in 2011, the DCDmethod excessively overestimates foggy pixels (2542 pixels). Among them, only 524 pixel are realfoggy pixels, but 331 pixels and 1687 pixels are clear and other type of clouds, respectively. The514 of real foggy pixels accounts for 70% of 749 foggy pixels identified by CALIPSO. Our proposednew algorithm detects foggy pixels by comparing the difference between cloud top temperatureand underneath sea surface temperature from assimilated data along with the DCD method. Wehave used two types of cloud top temperature, which obtained from 11 μm brightness temperature(B_S1) and operational COMS algorithm (B_S2). The detected foggy 1794 pixels from B_S1 and1490 pixel from B_S2 are significantly reduced the overestimation detected by the DCD method. However, 477 and 446 pixels have been found to be real foggy pixels, 329 and 264 pixels be clear,and 989 and 780 pixels be other type of clouds, detected by B_S1 and B_S2 respectively. The analysisof the operational COMS fog detection algorithm reveals that the cloud screening process wasstrictly enforced, which resulted in underestimation of foggy pixel. The 538 of total detected foggypixels obtain only 187 of real foggy pixels, but 61 of clear pixels and 290 of other type clouds. Ouranalysis suggests that there is no winner for nighttime sea fog detection algorithms, but loserbecause real foggy pixels are less than 30% among the foggy pixels declared by all algorithms. Thisoverwhelming evidence reveals that current nighttime sea fog algorithms have provided a lot of misjudgedinformation, which are mostly originated from difficulty in distinguishing between clear andcloudy scene as well as fog and other type clouds. Therefore, in-depth researches are urgentlyrequired to reduce the enormous error in nighttime sea fog detection from satellite.
Abstract
Nighttime sea fog detection from satellite is very hard due to limitation in using visiblechannels. Currently, most widely used method for the detection is the Dual Channel Difference(DCD) method based on Brightness Temperature Difference between 3.7 and 11 μm channel(BTD). However, this method have difficulty in distinguishing between fog and low cloud, andsometimes misjudges middle/high cloud as well as clear scene as fog. Using CALIPSO Lidar Profilemeasurements, we have analyzed the intrinsic problems in detecting nighttime sea fog from varioussatellite remote sensing algorithms and suggested the direction for the improvement of thealgorithm. From the comparison with CALIPSO measurements for May-July in 2011, the DCDmethod excessively overestimates foggy pixels (2542 pixels). Among them, only 524 pixel are realfoggy pixels, but 331 pixels and 1687 pixels are clear and other type of clouds, respectively. The514 of real foggy pixels accounts for 70% of 749 foggy pixels identified by CALIPSO. Our proposednew algorithm detects foggy pixels by comparing the difference between cloud top temperatureand underneath sea surface temperature from assimilated data along with the DCD method. Wehave used two types of cloud top temperature, which obtained from 11 μm brightness temperature(B_S1) and operational COMS algorithm (B_S2). The detected foggy 1794 pixels from B_S1 and1490 pixel from B_S2 are significantly reduced the overestimation detected by the DCD method. However, 477 and 446 pixels have been found to be real foggy pixels, 329 and 264 pixels be clear,and 989 and 780 pixels be other type of clouds, detected by B_S1 and B_S2 respectively. The analysisof the operational COMS fog detection algorithm reveals that the cloud screening process wasstrictly enforced, which resulted in underestimation of foggy pixel. The 538 of total detected foggypixels obtain only 187 of real foggy pixels, but 61 of clear pixels and 290 of other type clouds. Ouranalysis suggests that there is no winner for nighttime sea fog detection algorithms, but loserbecause real foggy pixels are less than 30% among the foggy pixels declared by all algorithms. Thisoverwhelming evidence reveals that current nighttime sea fog algorithms have provided a lot of misjudgedinformation, which are mostly originated from difficulty in distinguishing between clear andcloudy scene as well as fog and other type clouds. Therefore, in-depth researches are urgentlyrequired to reduce the enormous error in nighttime sea fog detection from satellite.
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