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朝鲜半岛冬季和春季颗粒物浓度的统计季节性预测。

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

作者信息

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

机构信息

Department of Climate and Energy Systems Engineering, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, South Korea.

Department of Marine Sciences and Convergent Technology, Hanyang University ERICA, Ansan, South Korea.

出版信息

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

DOI:10.1007/s13143-022-00275-4
PMID:35371395
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8960088/
Abstract

UNLABELLED

Concentrations of fine particulate matter smaller than 2.5 μm in diameter (PM) 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 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 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 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 concentration and an extreme metric, i.e., seasonal number of high PM concentration days.

SUPPLEMENTARY INFORMATION

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

摘要

未标注

由于气候条件的年际变化，朝鲜半岛上空直径小于2.5微米的细颗粒物（PM）浓度逐年变化。本研究基于缓慢变化的边界条件开发了一个多元线性回归模型，以提前1 - 3个月预测冬季和春季的PM浓度。利用首尔地方政府的观测数据，将始于2015年的韩国全国观测数据回溯至2005年，构建了一个涵盖2005 - 2019年的长期数据集。使用向前选择逐步回归方法，我们确定海表面温度（SST）、土壤湿度和2米气温作为模型的预测因子，同时由于与季节性PM浓度的相关性较弱而排除海冰浓度和积雪深度。对于冬季（12月 - 1月 - 2月，DJF），基于赤道大西洋上空的海表面温度和东欧上空的土壤湿度以及线性PM浓度趋势的模型生成的3个月预测与观测值的相关性为0.69。对于春季（3月 - 4月 - 5月，MAM），使用北太平洋上空的海表面温度和东亚上空的2米气温的模型精度提高到0.75。此外，我们发现季节性平均PM浓度与一个极端指标，即高PM浓度天数的季节数量之间存在线性关系。

补充信息

在线版本包含可在10.1007/s13143 - 022 - 00275 - 4获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/25da42e0a6d4/13143_2022_275_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/d550b0ffbf8b/13143_2022_275_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/cea9cc323edf/13143_2022_275_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/3be6ba0f87c6/13143_2022_275_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/63fe5eb3faf0/13143_2022_275_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/ae7cec502e19/13143_2022_275_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/d6a8d7eb5550/13143_2022_275_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/25da42e0a6d4/13143_2022_275_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/d550b0ffbf8b/13143_2022_275_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/4693c809352f/13143_2022_275_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/91b6c7e978d6/13143_2022_275_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/cea9cc323edf/13143_2022_275_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/3be6ba0f87c6/13143_2022_275_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/63fe5eb3faf0/13143_2022_275_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/ae7cec502e19/13143_2022_275_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/d6a8d7eb5550/13143_2022_275_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54f2/8960088/25da42e0a6d4/13143_2022_275_Fig9_HTML.jpg

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