Tornadoes are powerful vortices, ranging from a few meters to a few hundred meters in diameter, which occur over both the land and the sea, as a part of the tornadic convective storms (TCSs). In Korea, the possibility of tornado occurrence is relatively low, especially over the land. It might be partly due to complex topography over the Korean Peninsula where approximately 70% of the land is covered by mountains; thus, possibly discouraging development of TCSs in contrast to the Great Plains in the US. Although tornadoes are not likely to occur in Korea, once they happen, they can cause serious damages with potential loss of life and signi？cant economic cost. In this study, we investigate an inland tornado case occurred in Goyang, Korea on 10 June 2014, focusing on the development mechanism of the TCS, by employing the Weather Research and Forecasting (WRF) model, version 3.6.1, with the dynamic core of Advanced Research WRF (ARW). We also investigate the impact of diversity in land cover composition on the development of this TCS by changing the land use types over the plain areas of the Korean Peninsula.

 

In air quality prediction, initial conditions, based on both atmospheric and aerosol/chemistry observations, are essentially required for a coupled atmosphere-chemistry model. In general, we have more accurate model results with a higher amount and quality of observations; however, forecast errors in a region of interest may grow from an initial error in a specific upstream region, primarily due to the lack of observations therein. Therefore, finding the upstream areas from which a small initial error can grow into significant forecast errors in the region of interest is essential for the strategic enhancement of observations to improve numerical air quality prediction. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) represents the initial error that can lead to the most significant error at the forecast time, which enables us to identify the sensitive regions as a considerable initial error value. Thus, CNOP-I is a suitable tool for targeted (adaptive) observations. The goal of this study is identifying the key variables for classifying the synoptic patterns for aerosol cases by using the principal component analysis.

 

Ms. Seungyeon Lee is from Seoul, South Korea. And currently She is doing PhD course under Professor Park Seon Ki from Ewha Womans University. She got a bachelor's and master's degree from Ewha Womans University. She has been involved in various research projects from master's to the present.  And the main research fields are Extreme weather events (Tornadic convective storm), Synoptic pattern classification with Principal Component Analysis (PCA) and K-mean classification method.