SCI Journal

[1] Kang, G., J.-J. Kim, D.-J. Kim, W. Choi and S.-J. Park, 2017: Development of a computational fluid dynamics model with tree drag
parameterizations: Application to pedestrian wind comfort in an urban area. Building and Environment, 124, 209-218  DOI: 10.1016/j.buildenv.2017.08.008.

[2] C Yoo, J Im, S Park, LJ Quackenbush, 2018: Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data, ISPRS journal of photogrammetry and remote sensing, 137, Pages 149-162,  https://doi.org/10.1016/j.isprsjprs.2018.01.018

 

[3] Jin-Ho Yoon, S-Y Simon Wang, Min-Hui Lo, and Wen-Ying Wu, 2018: Concurrent increases in wet and dry extremes projected in Texas and combined effects on groundwater, Environmental Research Letters, 13, Number 5,  https://iopscience.iop.org/article/10.1088/1748-9326/aab96b/meta

[4] Donghyuck YoonDong‐Hyun Cha, Gil LeeChangyong ParkMyong‐In Lee, Ki‐Hong Min, 2018: Impacts of Synoptic and Local Factors on Heat Wave Events Over Southeastern Region of Korea in 2015, Journal of Geophysical research,  https://doi.org/10.1029/2018JD029247

[5] Nakbin Choi and Myong-In Lee, 2019: Spatial variability and long-term trend in the occurrence frequency of heatwave and tropical night in Korea, Asia-Pacific Journal of Atmospheric Sciences, 55(1), 101-114.

[6] Kim, M., Park, M. S., Im, J., Park, S., & Lee, M. I., 2019: Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data. Remote Sensing, 11(10), 1195.

[7] Kim, H., Lee, M. I., Cha, D. H., Lim, Y. K., & Putman, W. M., 2019: Improved representation of the diurnal variation of warm season precipitation by an atmospheric general circulation model at a 10 km horizontal resolution. Climate Dynamics, 1-20.

[8] Choi, N., Kim, K. M., Lim, Y. K., & Lee, M. I. (2019). Decadal changes in the leading patterns of sea level pressure in the Arctic and their impacts on the sea ice variability in boreal summer. The Cryosphere, 13(11), 3007-3021.

[9] Yoo, C., Han, D., Im, J., & Bechtel, B. (2019). Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 155-170.

 

[10] Park, S., Park, H., Im, J., Yoo, C., Rhee, J., Lee, B., & Kwon, C. (2019). Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches. PloS one, 14(10).

[11] Choi, N., Lee, M. I., Cha, D. H., Lim, Y. K., & Kim, K. M. (2019). Decadal Changes in the Interannual Variability of Heatwaves in East Asia Caused by Atmospheric Teleconnection Changes. Journal of Climate, (2019)

[12] Lee, M., Cha, D. H., Moon, J., Park, J., Jin, C. S., & Chan, J. C. (2019). Long‐term trends in tropical cyclone tracks around Korea and Japan in late summer and early fall. Atmospheric Science Letters, 20(11).

[13] Min, K. H., Chia‐Hui, C., Bae, J. H., & Cha, D. H. (2019). Synoptic characteristics of extreme heat waves over the Korean Peninsula based on ERA Interim reanalysis data. International Journal of Climatology.

[14] Shin,M., Kang, Y., Park, S., Im, J., Yoo, C., L..Quackenbush. (2019) . Satellite-based estimation of ground-level particulate matter concentrations: A review. GIScience and Remote Sensing

non-SCI

[1] 유철희, 2017: 폭염, 어떻게 대비할 것인가? 위성과 인공지능의 활용, 한국방재학회, 17(4)

[2] 유철희, 2017: 기계학습 기반 상세화를 통한 위성 지표면온도와 환경부 토지피복도를 이용한 열환경 분석: 대구광역시를 중심으로, Korean Journal of Remote Sensing, Vol.33, No.6-2, 2017

[3] 강정은, 2018: GIS 자료를 활용한 도시 재개발 주변 지역의 일조 환경 분석, Korean Journal of Remote Sensing, Vol.34, No.5, 2018

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