Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula
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 p...
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ftpubmed:oai:pubmedcentral.nih.gov:8960088 2023-05-15T18:18:35+02:00 Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula Jeong, Dajeong Yoo, Changhyun Yeh, Sang-Wook Yoon, Jin-Ho Lee, Daegyun Lee, Jae-Bum Choi, Jin-Young 2022-03-28 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960088/ https://doi.org/10.1007/s13143-022-00275-4 en eng Korean Meteorological Society http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960088/ http://dx.doi.org/10.1007/s13143-022-00275-4 © The Author(s) under exclusive licence to Korean Meteorological Society and Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Asia Pac J Atmos Sci Original Article Text 2022 ftpubmed https://doi.org/10.1007/s13143-022-00275-4 2022-04-03T01:07:24Z 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. Text Sea ice PubMed Central (PMC) Pacific Asia-Pacific Journal of Atmospheric Sciences |
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Original Article Jeong, Dajeong Yoo, Changhyun Yeh, Sang-Wook Yoon, Jin-Ho Lee, Daegyun Lee, Jae-Bum Choi, Jin-Young Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula |
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Original Article |
description |
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. |
format |
Text |
author |
Jeong, Dajeong Yoo, Changhyun Yeh, Sang-Wook Yoon, Jin-Ho Lee, Daegyun Lee, Jae-Bum Choi, Jin-Young |
author_facet |
Jeong, Dajeong Yoo, Changhyun Yeh, Sang-Wook Yoon, Jin-Ho Lee, Daegyun Lee, Jae-Bum Choi, Jin-Young |
author_sort |
Jeong, Dajeong |
title |
Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula |
title_short |
Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula |
title_full |
Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula |
title_fullStr |
Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula |
title_full_unstemmed |
Statistical Seasonal Forecasting of Winter and Spring PM(2.5) Concentrations Over the Korean Peninsula |
title_sort |
statistical seasonal forecasting of winter and spring pm(2.5) concentrations over the korean peninsula |
publisher |
Korean Meteorological Society |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960088/ https://doi.org/10.1007/s13143-022-00275-4 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Asia Pac J Atmos Sci |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8960088/ http://dx.doi.org/10.1007/s13143-022-00275-4 |
op_rights |
© The Author(s) under exclusive licence to Korean Meteorological Society and Springer Nature B.V. 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
op_doi |
https://doi.org/10.1007/s13143-022-00275-4 |
container_title |
Asia-Pacific Journal of Atmospheric Sciences |
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1766195215233712128 |