A22B-05
Development of a Numerical System to Improve Particulate Matter Forecasts in South Korea Using Geostationary Satellite-retrieved Aerosol Optical Data over Northeast Asia
Tuesday, 15 December 2015: 11:20
3006 (Moscone West)
Sojin Lee1, Chul H. Song1,2, RaeSeol Park2,3, Mi Eun Park2,4, Kyung Man Han2, Jhoon Kim5, Myungje Choi5, Young Sung Ghim6 and Jung-Hun Woo7, (1)GIST Gwangju Institute of Science and Technology, Gwangju, South Korea, (2)GIST Gwangju Institute of Science and Technology, School of Environmental Science and Engineering, Gwangju, South Korea, (3)KIAPS Korea Insititute of Atmospheric Prediction Systems, Numerical Model Team, Seoul, South Korea, (4)NIMR National Institute of Meterological Research, Asian Dust Research Division, Seoul, South Korea, (5)Yonsei University, Seoul, South Korea, (6)Hankuk University of Foreign Studies, Yongin, South Korea, (7)Konkuk University, Dept. of Advanced Technology Fusion, Seoul, South Korea
Abstract:
To improve short-term particulate matter (PM) forecasts in South Korea, the initial distribution of PM composition, particularly over the upwind regions, is primarily important. To prepare the initial PM composition, the aerosol optical depth (AOD) data retrieved from a geostationary equatorial orbit (GEO) satellite sensor, GOCI (Geostationary Ocean Color Imager) which covers Northeast Asia (113°E–146°E; 25°N–47°N), were used. A spatio-temporal (ST) kriging method was used to better prepare the initial AOD distributions that were converted into the PM composition over Northeast Asia. One of the largest advantages to using the ST-kriging method in this study is that more observed AOD data can be used to prepare the best initial AOD fields. It is demonstrated in this study that the short-term PM forecast system developed with the application of the ST-kriging method can greatly improve PM10 predictions in Seoul Metropolitan Area (SMA), when evaluated with ground-based observations. For example, errors and biases of PM10 predictions decreased by ~60% and ~70%, respectively, during the first 6 h of short-term PM forecasting, compared with those without the initial PM composition. In addition, The influences of several factors (such as choices of observation operators and control variables) on the performances of the short-term PM forecast were also explored in this study.
