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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
- Probability Index of Low Stratus and Fog at Dawn using Dual Geostationary Satellite Observations from COMS and FY-2D near the Korean Peninsula
- Yang, Jung-Hyun; Yoo, Jung-Moon; Choi, Yong-Sang; Wu, Dong; Jeong, Jin-Hee
- Ewha Authors
- 유정문; 최용상
- SCOPUS Author ID
- 유정문; 최용상
- Issue Date
- Journal Title
- REMOTE SENSING
- REMOTE SENSING vol. 11, no. 11
- fog; LSF; dawn; probability index; COMS; FY-2D; remote sensing; threshold; radiative transfer model
- SCIE; SCOPUS
- Document Type
- 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 degrees E); and the Chinese FengYun satellite (FY-2D; 86.5 degrees 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 m (R-0.67) from COMS, and 2) the FY-2D R-0.67 minus the COMS R-0.67 (oR(0.67)) and 3) the FY-2D-COMS difference in the brightness temperature difference between 3.7 and 11.0 m (BTD3.7-11). The three variables, adopted from the top three probability of detection (POD) scores for their fog detection thresholds: oR(0.67) (0.82) > BTD3.7-11 (0.73) > R-0.67 (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., R-0.67 or BTD3.7-11), so that this improvement was due to oR(0.67) and BTD3.7-11. 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.
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