top of page


IRIS 일러스트.png


✨ Research Overview


Tropical cyclone, Convection, Cloud, Rainfall prediction


Tropical cyclone trajectory and intensity

Short term precipitation

Convectice initiation


Tropical cyclone center and intensity

Overshooting tops

Icing detection

📚 Representative Works

* Staying the mouse over the screen stops the movement

Tropical Cyclone

Genesis Detection

A tropical cyclone (TC) is one of the most extreme disasters accompanied by harsh rainfall and strong winds. Detection and prediction of matured TCs are important, but early detection of the genesis stage is also important for longer preparation.

Of the thousands of disturbances each year, only dozens of TCs develop. Due to the complexity of the atmospheric system, it is quite difficult to sort develop and non-developing disturbance. Therefore, in our study, big data and artificial intelligence approaches were applied to build a cyclone genesis detection model.


  • Investigation of tropical cyclone genesis using geostationary satellite

  • Characterization and analysis of the tropical disturbances

In progress


📝 Publications

Last updated : 2021/01/10

Lee, J., Im, J., Cha, D. H., Park, H., & Sim, S. (2020). Tropical cyclone intensity estimation using multi-dimensional convolutional neural networks from geostationary satellite data. Remote Sensing, 12(1), 108.

Han, D., Lee, J., Im, J., Sim, S., Lee, S., & Han, H. (2019). A novel framework of detecting convective initiation combining automated sampling, machine learning, and repeated model tuning from geostationary satellite data. Remote Sensing, 11(12), 1454.

Lee, Y., Han, D., Ahn, M. H., Im, J., & Lee, S. J. (2019). Retrieval of total precipitable water from Himawari-8 AHI data: a comparison of random forest, extreme gradient boosting, and deep neural network. Remote Sensing, 11(15), 1741.

Sim, S., Im, J., Park, S., Park, H., Ahn, M. H., & Chan, P. W. (2018). Icing detection over East Asia from geostationary satellite data using machine learning approaches. Remote Sensing, 10(4), 631.

Kim, M., Lee, J., & Im, J. (2018). Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data. GIScience & Remote Sensing, 55(5), 763-792.

Kim, M., Im, J., Park, H., Park, S., Lee, M. I., & Ahn, M. H. (2017). Detection of tropical overshooting cloud tops using himawari-8 imagery. Remote sensing, 9(7), 685.

Lee, S., Han, H., Im, J., Jang, E., & Lee, M. I. (2017). Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data. Atmospheric Measurement Techniques, v.10, no.5, pp.1559-1574.

Park, M. S., Kim, M., Lee, M. I., Im, J., & Park, S. (2016). Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees. Remote sensing of environment, 183, 205-214.

Han, H., Lee, S., Im, J., Kim, M., Lee, M. I., Ahn, M. H., & Chung, S. R. (2015). Detection of convective initiation using Meteorological Imager onboard Communication, Ocean, and Meteorological Satellite based on machine learning approaches. Remote Sensing, 7(7), 9184-9204.

bottom of page