The deadline to submit a session, town hall, or pre-conference workshop proposal for AGU25 has passed. Please search for and review proposed sessions below. Please note this site is frozen as of 4/29/25 and will not reflect any changes to proposals made after this date.


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Abstract: Machine Learning-Based Bias Correction for Improving PM2.5 Prediction Performance Using the Community Multiscale Air Quality (CMAQ) Model (AGU25)

Machine Learning-Based Bias Correction for Improving PM2.5 Prediction Performance Using the Community Multiscale Air Quality (CMAQ) Model

Seong-il Lee1, Hyo-Jong Song2, Hye-Ryun Oh3 and Youngchae Kwon2, (1)Myongji University, Yongin-si, South Korea, (2)Myongji University, Dept. of Environmental Engineering and Energy, Seoul, South Korea, (3)Kyungpook National University, Department of Earth Science Education, Daegu, South Korea