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 (ΔR0.67) 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: ΔR0.67 (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 ΔR0.67 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.
Bibliographical noteFunding Information:
Funding: This work was supported by “Development of Cloud/Precipitation Algorithms” project, funded by ETRI (Electronics and Telecommunications Research Institute), which is a subproject of the “Development of Geostationary Meteorological Satellite Ground Segment (NMSC-2019-01)” program funded by the NMSC (National Meteorological Satellite Center) of KMA (Korea Meteorological Administration).
© 2019 by the authors.
- Probability index
- Radiative transfer model
- Remote sensing