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Journal of Atmospheric Pollution
ISSN (Print): 2381-2982 ISSN (Online): 2381-2990 Website: https://www.sciepub.com/journal/jap Editor-in-chief: Ki-Hyun Kim
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Journal of Atmospheric Pollution. 2024, 10(1), 1-8
DOI: 10.12691/jap-10-1-1
Open AccessArticle

Determining the Effectiveness of the Superensemble for Atmospheric Concentration Prediction

Frank Gronwald1, and Shoou-Yuh Chang1

1North Carolina A & T University

Pub. Date: August 09, 2024
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Cite this paper:
Frank Gronwald and Shoou-Yuh Chang. Determining the Effectiveness of the Superensemble for Atmospheric Concentration Prediction. Journal of Atmospheric Pollution. 2024; 10(1):1-8. doi: 10.12691/jap-10-1-1

Abstract

The need for government regulators and industrialized sources to determine the level of air pollution is essential. Air dispersion models are often used to determine the concentration of a pollutant. However changing conditions and several assumptions made by the models limit their accuracy at various times. This research was performed by combining four different air dispersion models (Gaussian Plume, Variable K Theory, Box, and AFTOX) into a superensemble. Since the superensemble is typically more accurate than its member models, the calculated result should be a more accurate prediction under any condition. One of the key parameters in the formation of the superensemble is whether the superensemble calculations for that range are to be fixed or continuous throughout. In the interest of evaluating performance, the change in accuracy for each member model and superensemble was measured through determining RMSE.

Keywords:
Superensemble Air Dispersion Modeling Brier Score RMSE

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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