Temperature Dependent Initial Chemical Conditions for WRF-Chem Air Pollution Simulation Model
Date Issued
2020-09-24
Author(s)
Anchev, Nenad
Abstract
Air pollution is a health hazard that has been brought to public attention in the recent years, due to the widespread networks of air quality measurement stations. The importance of the problem brought the need to develop accurate air prediction models. The coupled meteo-chemical simulation systems
have already been demonstrated to correctly predict the episodes of high pollution events. Due to the complexity of these models, which simulate the emissions, interactions and transport of pollutants in the atmosphere, setting up the
correct parameters tailored for a specific area is a challenging task. In this paper
we present an exhaustive analysis of the historical air pollution measurements, a
detailed evaluation of an existing WRF-Chem based predictive model and propose an approach for improvement of that specific model. We use a specific
temperature-dependent way of scaling the initial chemical conditions of a
WRF-chem simulation, which leads to significant reduction of the bias by the
model. We present the analysis that led us into these conclusions, the setup of
the model, and the improvements made by using this approach.
have already been demonstrated to correctly predict the episodes of high pollution events. Due to the complexity of these models, which simulate the emissions, interactions and transport of pollutants in the atmosphere, setting up the
correct parameters tailored for a specific area is a challenging task. In this paper
we present an exhaustive analysis of the historical air pollution measurements, a
detailed evaluation of an existing WRF-Chem based predictive model and propose an approach for improvement of that specific model. We use a specific
temperature-dependent way of scaling the initial chemical conditions of a
WRF-chem simulation, which leads to significant reduction of the bias by the
model. We present the analysis that led us into these conclusions, the setup of
the model, and the improvements made by using this approach.
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