High Resolution Air Quality Forecasting Over Prague within the URBI PRAGENSI Project: Model Performance During the Winter Period and the Effect of Urban Parameterization on PM

• Main Authors: Jana Ďoubalová, Peter Huszár, Kryštof Eben, Nina Benešová, Michal Belda, Ondřej Vlček, Jan Karlický, Jan Geletič, Tomáš Halenka
• Format: Article
• Language: English
• Published: MDPI AG 2020-06-01
• Series: Atmosphere
• Subjects: air pollution; emissions; urban canopy; weather prediction; particulate matter; validation
• Online Access: https://www.mdpi.com/2073-4433/11/6/625

The overall impact of urban environments on the atmosphere is the result of many different nonlinear processes, and their reproduction requires complex modeling approaches. The parameterization of these processes in the models can have large impacts on the model outputs. In this study, the evaluation of a WRF/Comprehensive Air Quality Model with Extensions (CAMx) forecast modeling system set up for Prague, the Czech Republic, within the project URBI PRAGENSI is presented. To assess the impacts of urban parameterization in WRF, in this case with the BEP+BEM (Building Environment Parameterization linked to Building Energy Model) urban canopy scheme, on Particulate Matter (PM) simulations, a simulation was performed for a winter pollution episode and compared to a non-urbanized run with BULK treatment. The urbanized scheme led to an average increase in temperature at 2 m by 2 <inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mo>∘</mo> </msup> </semantics> </math> </inline-formula>C, a decrease in wind speed by 0.5 m s<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics> </math> </inline-formula>, a decrease in relative humidity by 5%, and an increase in planetary boundary layer height by 100 m. Based on the evaluation against observations, the overall model error was reduced. These impacts were propagated to the modeled PM concentrations, reducing them on average by 15–30 <inline-formula> <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math> </inline-formula>g m<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics> </math> </inline-formula> and 10–15 <inline-formula> <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math> </inline-formula>g m<inline-formula> <math display="inline"> <semantics> <msup> <mrow></mrow> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </semantics> </math> </inline-formula> for PM<inline-formula> <math display="inline"> <semantics> <msub> <mrow></mrow> <mn>10</mn> </msub> </semantics> </math> </inline-formula> and PM<inline-formula> <math display="inline"> <semantics> <msub> <mrow></mrow> <mrow> <mn>2</mn> <mo>.</mo> <mn>5</mn> </mrow> </msub> </semantics> </math> </inline-formula>, respectively. In general, the urban parameterization led to a larger underestimation of the PM values, but yielded a better representation of the diurnal variations.