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http://dx.doi.org/10.15207/JKCS.2021.12.10.063

Convergence of Artificial Intelligence Techniques and Domain Specific Knowledge for Generating Super-Resolution Meteorological Data  

Ha, Ji-Hun (IT Division, Korea Oceanic & Atmospheric System Technology)
Park, Kun-Woo (IT Division, Korea Oceanic & Atmospheric System Technology)
Im, Hyo-Hyuk (Korea Oceanic & Atmospheric System Technology)
Cho, Dong-Hee (Department of Computer Science, Kwangwoon University)
Kim, Yong-Hyuk (Department of Computer Science, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.12, no.10, 2021 , pp. 63-70 More about this Journal
Abstract
Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.
Keywords
Meteorology data; High-resolution data generation; Objective analysis; Machine learning; SRGAN;
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