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Fog Detection Using Geostationary Satellite Data: Temporally Continuous Algorithm
이정림,정추용,오미림 한국기상학회 2011 Asia-Pacific Journal of Atmospheric Sciences Vol.47 No.2
A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 μm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 μm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 μm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 μm and 10.8 μm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 μm)channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds
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김희영,박경애,정성래,백선균,이병일,신인철,정추용,김재관,정원찬 대한원격탐사학회 2018 大韓遠隔探査學會誌 Vol.34 No.1
Passive microwave sea surface temperatures (SST) were validated in the Northwest Pacific using a total of 102,294 collocated matchup data between Global Precipitation Measurement (GPM) / GPM Microwave Sensor (GMI) data and oceanic in-situ temperature measurements from March 2014 to December 2016. A root-mean-square (RMS) error and a bias error of the GMI SST measurements were evaluated to 0.93°C and 0.05°C, respectively. The SST differences between GMI and in-situ measurements were caused by various factors such as wind speed, columnar atmospheric water vapor, land contamination near coastline or islands. The GMI SSTs were found to be higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. As the wind speed increased at night, SST errors showed positive bias. In addition, other factors, coming from atmospheric water vapor, sensitivity degradation at a low temperature range, and land contamination, also contributed to the errors. One of remarkable characteristics of the errors was their latitudinal dependence with large errors at high latitudes above 30°N. Seasonal characteristics revealed that the errors were most frequently observed in winter with a significant positive deviation. This implies that SST errors tend to be large under conditions of high wind speeds and low SSTs. Understanding of microwave SST errors in this study is anticipated to compensate less temporal capability of Infrared SSTs and to contribute to increase a satellite observation rate with time, especially in SST composite process.
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