Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability

Author:

Yu JinhyeokORCID,Song Chul H.,Lee Dogyeong,Lee Sojin,Kim Hyun S.,Han Kyung M.,Park Seohui,Im JunghoORCID,Park Soon-YoungORCID,Jeon Moongu,Peuch Vincent-HenriORCID,Saide Pablo E.,Carmichael Gregory R.,Kim JeehoORCID,Kim JhoonORCID,Song Chang-KeunORCID,Woo Jung-Hun,Ryu Seong-Hyun

Abstract

AbstractConcentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations.

Funder

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

Atmospheric Science,Environmental Chemistry,Global and Planetary Change

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Analysis and applications of the geostationary environment monitoring spectrometer (GEMS) data;Remote Sensing of Clouds and the Atmosphere XXVIII;2023-10-19

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