(Heatwave Research Center)

[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.

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[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

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[4] Donghyuck Yoon, Dong‐Hyun Cha, Gil Lee, Changyong Park, Myong‐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

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[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.

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[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.

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[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.

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[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.

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[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).

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[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)

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[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).

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[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.

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[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

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[15] Cho, D., Yoo, C., Im, J., & Cha, D. H. (2020). Comparative assessment of various machine learning‐based bias correction methods for numerical weather prediction model forecasts of extreme air temperatures in urban areas. Earth and Space Science, 7(4), e2019EA000740.

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[16] Cho, D., Yoo, C., Im, J., Lee, Y., & Lee, J. (2020). Improvement of spatial interpolation accuracy of daily maximum air temperature in urban areas using a stacking ensemble technique. GIScience & Remote Sensing, 57(5), 633-649.

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[17] Park, S., Kang, D., Yoo, C., Im, J., & Lee, M. I. (2020). Recent ENSO influence on East African drought during rainy seasons through the synergistic use of satellite and reanalysis data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 17-26.

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[18] Kim, M., Kim, H. C., Im, J., Lee, S., & Han, H. (2020). Object-based landfast sea ice detection over West Antarctica using time series ALOS PALSAR data. Remote Sensing of Environment, 242, 111782.

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[19] Park, S., Lee, J., Im, J., Song, C. K., Choi, M., Kim, J., ... & Quackenbush, L. J. (2020). Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models. Science of the Total Environment, 713, 136516.

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[20] Yoo, C., Im, J., Cho, D., Yokoya, N., Xia, J., & Bechtel, B. (2020). Estimation of all-weather 1 km MODIS land surface temperature for humid summer days. Remote Sensing, 12(9), 1398.

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[21] Shin, M., Kang, Y., Park, S., Im, J., Yoo, C., & Quackenbush, L. J. (2020). Estimating ground-level particulate matter concentrations using satellite-based data: A review. GIScience & Remote Sensing, 57(2), 174-189.

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[22] Kim, Y. J., Kim, H. C., Han, D., Lee, S., & Im, J. (2020). Prediction of monthly Arctic sea ice concentrations using satellite and reanalysis data based on convolutional neural networks. The Cryosphere, 14(3), 1083-1104.

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[23] Choi, N., Lee, M. I., Cha, D. H., Lim, Y. K., & Kim, K. M. (2020). Decadal changes in the interannual variability of heat waves in East Asia caused by atmospheric teleconnection changes. Journal of Climate, 33(4), 1505-1522.

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[24] Lee, J. G., Min, K. H., Park, H., Kim, Y., Chung, C. Y., & Chang, E. C. (2020). Improvement of the rapid-development thunderstorm (RDT) algorithm for use with the GK2A satellite. Asia-Pacific Journal of Atmospheric Sciences, 56(2), 307-319.

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[25] Seo, E., Lee, M. I., Schubert, S. D., Koster, R. D., & Kang, H. S. (2020). Investigation of the 2016 Eurasia heat wave as an event of the recent warming. Environmental Research Letters, 15(11), 114018.

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[26] Yoon, D., Cha, D. H., Lee, M. I., Min, K. H., Kim, J., Jun, S. Y., & Choi, Y. (2020). Recent changes in heatwave characteristics over Korea. Climate Dynamics, 55(7), 1685-1696.

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[27] Min, K. H., Chung, C. H., Bae, J. H., & Cha, D. H. (2020). Synoptic characteristics of extreme heatwaves over the Korean Peninsula based on ERA Interim reanalysis data. International Journal of Climatology, 40(6), 3179-3195.

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[28] Wang, J. W., Yang, H. J., & Kim, J. J. (2020). Wind speed estimation in urban areas based on the relationships between background wind speeds and morphological parameters. Journal of Wind Engineering and Industrial Aerodynamics, 205, 104324.

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[29] Kim, D. J., Kang, G., Kim, D. Y., & Kim, J. J. (2020). Characteristics of LDAPS-predicted surface wind speed and temperature at automated weather stations with different surrounding land cover and topography in Korea. Atmosphere, 11(11), 1224.

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[30] Yoo, C., Lee, Y., Cho, D., Im, J., & Han, D. (2020). Improving local climate zone classification using incomplete building data and Sentinel 2 images based on convolutional neural networks. Remote Sensing, 12(21), 3552.

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[31] Son, B., Park, S., Im, J., Park, S., Ke, Y., & Quackenbush, L. J. (2021). A new drought monitoring approach: Vector Projection Analysis (VPA). Remote Sensing of Environment, 252, 112145.

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[32] Park, S. J., Kim, J. J., Choi, W., Kim, E. R., Song, C. K., & Pardyjak, E. R. (2020). Flow characteristics around step-up street canyons with various building aspect ratios. Boundary-Layer Meteorology, 174(3), 411-431.

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[33] Yoon, D., Cha, D. H., Lee, M. I., Min, K. H., Jun, S. Y., & Choi, Y. (2021). Comparison of regional climate model performances for different types of heat waves over South Korea. Journal of Climate, 34(6), 2157-2174.

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[34] Lee, J. W., Min, K. H., & Lim, K. S. S. (2022). Comparing 3DVAR and hybrid radar data assimilation methods for heavy rain forecast. Atmospheric Research, 270, 106062.

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[35] Bae, J. H., & Min, K. H. (2022). Forecast Characteristics of Radar Data Assimilation Based on the Scales of Precipitation Systems. Remote Sensing, 14(3), 605.

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[36] Yoon, D., Kim, K., Cha, D. H., Lee, M. I., Im, J., Cho, D., & Min, K. H. (2022). Development of model output statistics based on the least absolute shrinkage and selection operator regression for forecasting next‐day maximum temperature in South Korea. Quarterly Journal of the Royal Meteorological Society.

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[37] Cho, D., Yoo, C., Son, B., Im, J., Yoon, D., & Cha, D. H. (2022). A novel ensemble learning for post-processing of NWP Model's next-day maximum air temperature forecast in summer using deep learning and statistical approaches. Weather and Climate Extremes, 35, 100410.

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[38] Cho, D., Bae, D., Yoo, C., Im, J., Lee, Y., & Lee, S. (2022). All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning. Remote Sensing, 14(8), 1815.

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[39] Yoo, C., Im, J., Cho, D., Lee, Y., Bae, D., & Sismanidis, P. (2022). Downscaling MODIS nighttime land surface temperatures in urban areas using ASTER thermal data through local linear forest. International Journal of Applied Earth Observation and Geoinformation, 110, 102827.

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[40] Lee, J., Park, S., Im, J., Yoo, C., & Seo, E. (2022). Improved soil moisture estimation: Synergistic use of satellite observations and land surface models over CONUS based on machine learning. Journal of Hydrology, 609, 127749.

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