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Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula

Authors :
Jung-Hyun Yang
Jung-Moon Yoo
Yong-Sang Choi
Dong Wu
Jin-Hee Jeong
Source :
Remote Sensing, Vol 11, Iss 11, p 1283 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

We developed a new remote sensing method for detecting low stratus and fog (LSF) at dawn in terms of probability index (PI) of LSF from simultaneous stereo observations of two geostationary-orbit satellites; the Korean Communication, Ocean, and Meteorological Satellite (COMS; 128.2°E); and the Chinese FengYun satellite (FY-2D; 86.5°E). The algorithm was validated near the Korean Peninsula between the months of April and August from April 2012 to June 2015, by using surface observations at 45 meteorological stations in South Korea. The optical features of LSF were estimated by using satellite retrievals and simulated data from the radiative transfer model. The PI was calculated using the combination of three satellite-observed variables: 1) the reflectance at 0.67 μm (R0.67) from COMS, and 2) the FY-2D R0.67 minus the COMS R0.67 (△R0.67) and 3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 μm (ΔBTD3.7-11). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: △R0.67 (0.82) > ΔBTD3.7-11 (0.73) > R0.67 (0.70) > BTD3.7-11 (0.51). The LSF PI for this algorithm was significantly better in the two case studies compared to that using COMS only (i.e., R0.67 or BTD3.7-11), so that this improvement was due to △R0.67 and ΔBTD3.7-11. Overall, PI in the LSF spatial distribution has the merits of a high detection rate, a specific probability display, and a low rate of seasonality and variability in detection accuracy. Therefore, PI would be useful for monitoring LSF in near-real-time, and to further its forecast ability, using next-generation satellites.

Details

Language :
English
ISSN :
20724292 and 11111283
Volume :
11
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.07863f0e17774c628839f52fde21cb06
Document Type :
article
Full Text :
https://doi.org/10.3390/rs11111283