[26] Luo, J. J., Xia, J., Pan, B., Ham, Y. G., Li, X., Shanguan, W., ... & Zhao, M. (2026). AI for atmosphere-ocean sciences: advancements, challenges, and ways forward. National Science Review, nwag063.

[25] Park, H., Park, S., Kang, D., & Kim, J. H. (2026). A super-resolution framework for downscaling machine learning weather prediction toward 1-km air temperature. npj Climate and Atmospheric Science.

[24] Kim, M., Kang, D., Sohn, S. J., Kim, G., Rhee, J., & Kim, S. (2026). Multi‐scale decomposition for skillful all‐season MJO prediction with deep learning. Geophysical Research Letters, 53(1), e2025GL117981.

[23] Cheon, M., Kim, J. H., Choi, Y., Choi, Y. H., Kang, S. Y., Lee, J. G., ... & Kang, D.* (2025). Understanding machine learning weather prediction by designing a cost-efficient model with knowledge-oriented modules. Scientific Reports.

[22] Back, S. Y., Kim, D., Son, S. W., & Kang, D. (2025). Interseasonal Difference in MJO Propagation during the Extended Boreal Winter: Observations and Climate Model Simulations. Journal of Climate, 38(22), 6693-6708.

[21] Lee, J. G., Kang, D., Kim, J. H., Kim, J. M., Lee, S. M., & Ham, Y. G. (2025). Data assimilation of satellite-derived Arctic sea ice thickness during boreal summer. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20] Shin, N. Y., Kang, D.*, Kim, D., Lee, J. Y., & Kug, J. S. (2024). Data-driven investigation on the boreal summer MJO predictability. npj Climate and Atmospheric Science, 7(1), 248.

[19] Jang, J., Baek, S. S., Kang, D., Park, Y., Ligaray, M., Baek, S. H., ... & Cho, K. H. (2024). Insights and machine learning predictions of harmful algal bloom in the East China Sea and Yellow Sea. Journal of Cleaner Production, 459, 142515.

[18] Shin, N. Y., Kim, D., Kang, D., Kim, H., & Kug, J. S. (2024). Deep learning reveals moisture as the primary predictability source of MJO. npj Climate and Atmospheric Science, 7(1), 11.

[17] Rushley, S. S., Kang, D., Kim, D., An, S. I., & Wang, T. (2023). MJO in Different Orbital Regimes: Role of the Mean State in the MJO’s Amplitude during Boreal Winter. Journal of Climate, 36(13), 4475-4490.

[16] Kim, J., Kang, D., Lee, M.-I., Jin, E. K., Kug, J.-S., & Lee, W. S. (2023). Remote influences of ENSO and IOD on the interannual variability of the West Antarctic sea ice. Journal of Geophysical Research: Atmospheres, 128, e2022JD038313.

[15] Kang, D., Kim, D., Rushley, S., & Maloney, E. (2022). Seasonal locking of the MJO’s southward detour of the Maritime Continent: the role of the Australian monsoon. Journal of Climate, 1-33.

[14] Yoo, C., Kang, D.*, & Park, S. (2022). Identifying the Impact of Regional Meteorological Parameters on US Crop Yield at Various Spatial Scales Using Remote Sensing Data. Remote Sensing, 14(15), 3508.

[13] Kim, D., Kang, D.*, Ahn, M. S., DeMott, C., Hsu, C. W., Yoo, C., ... & Rasch, P. J. (2022). The Madden–Julian Oscillation in the Energy Exascale Earth System Model Version 1. Journal of Advances in Modeling Earth Systems, 14(2), e2021MS002842.

[12] Kang, D., Kim, D., Ahn, M. S., & An, S. I. (2021). The Role of the Background Meridional Moisture Gradient on the Propagation of the MJO over the Maritime Continent. Journal of Climate, 34(16), 6565-6581. https://doi.org/10.1175/JCLI-D-20-0085.1

[11] Ren, P., Kim, D., Ahn, M. S., Kang, D., & Ren, H. L. (2021). Intercomparison of MJO column moist static energy and water vapor budget among six modern reanalysis products. Journal of Climate, 34(8), 2977-3001. https://doi.org/10.1175/JCLI-D-20-0653.1

[10] Kang, D., D. Kim, M.-S. Ahn, R. Neale, J. Lee, and P. Glecker, 2020: The role of the mean state on MJO simulation in CESM2 ensemble simulation. Geophysical Research Letters, 47(24), e2020GL089824. https://doi.org/10.1029/2020GL089824

[9] Ahn, M. S., Kim, D., Kang, D., Lee, J., Sperber, K. R., Gleckler, P. J., ... & Kim, H. (2020). MJO Propagation across the Maritime Continent: Are CMIP6 Models Better than CMIP5 Models?. Geophysical Research Letters, 47(11), e2020GL087250. https://doi.org/10.1029/2020GL087250

[8] 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. https://doi.org/10.1016/j.isprsjprs.2020.02.003

[7] Kang, D., & Lee, M. I. (2019). ENSO influence on the dynamical seasonal prediction of the East Asian Winter Monsoon. Climate Dynamics, 53(12), 7479-7495. https://doi.org/10.1007/s00382-017-3574-4

[6] Park, S., Seo, E., Kang, D., Im, J., & Lee, M. I. (2018). Prediction of drought on pentad scale using remote sensing data and MJO index through random forest over East Asia. Remote Sensing, 10(11), 1811. https://doi.org/10.3390/rs10111811

[5] Timmermann, A., An, S., Kug, J. et al., (2018), El Nino-Southern Oscillation Complexity, Nature, 559, 535-545. https://doi.org/10.1038/s41586-018-0252-6

[4] Kang, D., & Lee, M. I. (2017). Increase in the potential predictability of the Arctic Oscillation via intensified teleconnection with ENSO after the mid-1990s. Climate Dynamics, 49(5-6), 2147-2160. https://doi.org/10.1007/s00382-016-3436-5

[3] Kang, D., Im, J., Lee, M. I., & Quackenbush, L. J. (2014). The MODIS ice surface temperature product as an indicator of sea ice minimum over the Arctic Ocean. Remote sensing of environment, 152, 99-108. https://doi.org/10.1016/j.rse.2014.05.012

[2] Kang, D., Lee, M. I., Im, J., Kim, D., Kim, H. M., Kang, H. S., ... & MacLachlan, C. (2014). Prediction of the Arctic Oscillation in boreal winter by dynamical seasonal forecasting systems. Geophysical Research Letters, 41(10), 3577-3585. https://doi.org/10.1002/2014GL060011

[1] Lee, M. I., Kang, H. S., Kim, D., Kim, D., Kim, H., & Kang, D. (2014). Validation of the experimental hindcasts produced by the GloSea4 seasonal prediction system. Asia-Pacific Journal of Atmospheric Sciences, 50(3), 307-326. https://doi.org/10.1007/s13143-014-0019-4