Journal of Information Science Theory and Practice

Korea Institute of Science and Technology Information (한국과학기술정보연구원 과학기술정보센터)
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Construction of a Spatio-Temporal Dataset for Deep Learning-Based Precipitation Nowcasting

  • Kim, Wonsu (Korea Institute of Science and Technology Information (KISTI)) ;
  • Jang, Dongmin (Korea Institute of Science and Technology Information (KISTI)) ;
  • Park, Sung Won (Korea Institute of Science and Technology Information (KISTI)) ;
  • Yang, MyungSeok (Korea Institute of Science and Technology Information (KISTI))
  • Received : 2022.04.27
  • Accepted : 2022.05.17
  • Published : 2022.06.20
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Abstract

Recently, with the development of data processing technology and the increase of computational power, methods to solving social problems using Artificial Intelligence (AI) are in the spotlight, and AI technologies are replacing and supplementing existing traditional methods in various fields. Meanwhile in Korea, heavy rain is one of the representative factors of natural disasters that cause enormous economic damage and casualties every year. Accurate prediction of heavy rainfall over the Korean peninsula is very difficult due to its geographical features, located between the Eurasian continent and the Pacific Ocean at mid-latitude, and the influence of the summer monsoon. In order to deal with such problems, the Korea Meteorological Administration operates various state-of-the-art observation equipment and a newly developed global atmospheric model system. Nevertheless, for precipitation nowcasting, the use of a separate system based on the extrapolation method is required due to the intrinsic characteristics associated with the operation of numerical weather prediction models. The predictability of existing precipitation nowcasting is reliable in the early stage of forecasting but decreases sharply as forecast lead time increases. At this point, AI technologies to deal with spatio-temporal features of data are expected to greatly contribute to overcoming the limitations of existing precipitation nowcasting systems. Thus, in this project the dataset required to develop, train, and verify deep learning-based precipitation nowcasting models has been constructed in a regularized form. The dataset not only provides various variables obtained from multiple sources, but also coincides with each other in spatio-temporal specifications.

Keywords

Acknowledgement

This paper was supported by the Ministry of Science and ICT, Republic of Korea (Project No. K-22-L04-C06-S01).

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