지역규모 대기질 모델 결과 평가를 위한 통계 검증지표 활용 - 미세먼지 모델링을 중심으로 - (A Study on Statistical Parameters for the Evaluation of Regional Air Quality Modeling Results – Focused on Fine Dust Modeling –)

김철희 (Kim, Cheol-Hee); 이상현 (Lee, Sang-Hyun); 장민 (Min Jang); 천성남 (Sung-Nam Chun); 강수지 (Suji Kang); 고광근 (Ko Kwang-Kun); 이종재 (Jong-Jae， Lee); 이효정 (Lee, Hyo-Jung)

A Study on Statistical Parameters for the Evaluation of Regional Air Quality Modeling Results – Focused on Fine Dust Modeling –

Journal of Environmental Impact Assessment
Abbr : J EIA
2020, 29(4), pp.272-285
Publisher : Korean Society Of Environmental Impact Assessment
Research Area : Engineering > Environmental Engineering
Received : April 24, 2020
Accepted : July 24, 2020
Published : August 31, 2020

Kim, Cheol-Hee ¹, Lee, Sang-Hyun ², Min Jang ³, Sung-Nam Chun ⁴, Suji Kang ⁴, Ko Kwang-Kun ⁵, Jong-Jae， Lee ⁶, Lee, Hyo-Jung ¹

¹부산대학교
²공주대학교
³한국외국어대학교
⁴한국전력공사 전력연구원
⁵연세대학교 동서문제연구원
⁶울산과학기술원

Accredited

ABSTRACT

We investigated statistical evaluation parameters for 3D meteorological and air quality models and selected several quantitative indicator references, and summarized the reference values of the statistical parameters for domestic air quality modeling researcher. The finally selected 9 statistical parameters are MB (Mean Bias), ME (Mean Error), MNB (Mean Normalized Bias Error), MNE (Mean Absolute Gross Error), RMSE (Root Mean Square Error), IOA (Index of Agreement), R (Correlation Coefficient), FE (Fractional Error), FB (Fractional Bias), and the associated reference values are summarized. The results showed that MB and ME have been widely used in evaluating the meteorological model output, and NMB and NME are most frequently used for air quality model results. In addition, discussed are the presentation diagrams such as Soccer Plot, Taylor diagram, and Q-Q (Quantile-Quantile) diagram. The current results from our study is expected to be effectively used as the statistical evaluation parameters suitable for situation in Korea considering various characteristics such as including the mountainous surface areas.

KEYWORDS

Statistical evaluation parameters, Model evaluation, Regional air quality model, PM2.5 modeling

Citation status

* References for papers published after 2023 are currently being built.

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Journal of Environmental Impact Assessment 2023 KCI Impact Factor : 0.51

A Study on Statistical Parameters for the Evaluation of Regional Air Quality Modeling Results – Focused on Fine Dust Modeling –

ABSTRACT

KEYWORDS

Citation status

* References for papers published after 2023 are currently being built.

Journal of Environmental Impact Assessment 2023 KCI Impact Factor : 0.51

A Study on Statistical Parameters for the Evaluation of Regional Air Quality Modeling Results – Focused on Fine Dust Modeling –

ABSTRACT

KEYWORDS

Statistics

Tools

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Citation status

KCI Citation Counts (4)

REFERENCES (33) * References for papers published after 2023 are currently being built.

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* References for papers published after 2023 are currently being built.