Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula

Dajeong Jeong; Changhyun Yoo; Sang-Wook Yeh; Jin-Ho Yoon; Daegyun Lee; Jae-Bum Lee; Jin-Young Choi

doi:10.1007/s13143-022-00275-4

Statistical Seasonal Forecasting of Winter and Spring PM_2.5 Concentrations Over the Korean Peninsula

Asia Pac J Atmos Sci. 2022;58(4):549-561. doi: 10.1007/s13143-022-00275-4. Epub 2022 Mar 28.

Authors

Dajeong Jeong¹, Changhyun Yoo¹, Sang-Wook Yeh², Jin-Ho Yoon³, Daegyun Lee⁴, Jae-Bum Lee⁴, Jin-Young Choi⁴

Affiliations

¹ Department of Climate and Energy Systems Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, South Korea.
² Department of Marine Sciences and Convergent Technology, Hanyang University ERICA, Ansan, South Korea.
³ School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea.
⁴ Air Quality Forecasting Center, National Institute of Environmental Research, Incheon, South Korea.

Abstract

Concentrations of fine particulate matter smaller than 2.5 μm in diameter (PM_2.5) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM_2.5 concentrations at 1-3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government's observations, constructing a long-term dataset covering the 2005-2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM_2.5 concentrations. For the wintertime (December-January-February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM_2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March-April-May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM_2.5 concentration and an extreme metric, i.e., seasonal number of high PM_2.5 concentration days.

Supplementary information: The online version contains supplementary material available at 10.1007/s13143-022-00275-4.

Keywords: Multiple linear regression model; PM2.5 concentrations; Seasonal prediction.