Meteorological Phenomena Driving Extreme Air Pollution

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (14 August 2020) | Viewed by 4576

Special Issue Editors


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Guest Editor
Department of Physics, University of Nevada, Reno, NV, USA
Interests: air quality; chemical transport models; atmospheric aerosols; satellite remote sensing; meteorology; numerical weather prediction; land–atmosphere exchange; atmospheric turbulence

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Guest Editor
1. Adjunct Professor, CSISS, George Mason University, 4400 University Dr., Fairfax, VA 22030, USA
2. NOAA/Air Resources Lab National Air Quality Forecasting Capability Project Leader, NCWCP, College Park, MD 20740, USA
Interests: process research; meteorological and atmospheric composition measurements that advance knowledge and prediction accuracy for air pollution events
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Special Issue Information

Dear Colleagues,

Atmospheric processes influence the chemistry, transport, and accumulation of air pollution worldwide. Meteorological phenomena create conditions that lead to extreme air pollution events or poor air quality episodes, with concentrations that are harmful to human health and the environment. Understanding the meteorological processes, on all scales, that create these extreme air pollution events is important for effective air quality forecasting and air pollution mitigation strategies. We invite original research articles, as well as review articles, that investigate the meteorological phenomena associated with extreme air pollution events. Contributions from field experiments; numerical modeling, including artificial intelligence and machine learning; or data science investigations are all welcome. Some suggested topics include, but are not limited to, the following:

  • Wintertime high-pressure subsidence leading to temperature inversions;
  • Meteorological and land/sea interface influences on ozone concentrations;
  • Wind direction patterns from mid-latitude cyclones that increase pollutant concentrations;
  • Meteorological conditions driving land surface and wind conditions for wildland fires;
  • Dust storms causing elevated coarse particulate matter concentrations.

Dr. Heather A. Holmes
Dr. Pius Lee
Guest Editors

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Keywords

  • Air quality;
  • Chemical transport model;
  • Meteorology;
  • Numerical weather prediction;
  • Microscale and urban meteorology;
  • Mesoscale meteorology;
  • Synoptic Meteorology.

Published Papers (1 paper)

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Research

20 pages, 8042 KiB  
Article
Impact of Effective Roughness Length on Mesoscale Meteorological Simulations over Heterogeneous Land Surfaces in Taiwan
by Fang-Yi Cheng, Chin-Fang Lin, Yu-Tzu Wang, Jeng-Lin Tsai, Ben-Jei Tsuang and Ching-Ho Lin
Atmosphere 2019, 10(12), 805; https://doi.org/10.3390/atmos10120805 - 12 Dec 2019
Cited by 5 | Viewed by 3899
Abstract
The Weather Research and Forecasting (WRF) modeling system obtains the aerodynamic roughness length (z0) from a land use (LU) lookup table. The effective aerodynamic roughness length (z0eff) was estimated for the island of Taiwan by considering the [...] Read more.
The Weather Research and Forecasting (WRF) modeling system obtains the aerodynamic roughness length (z0) from a land use (LU) lookup table. The effective aerodynamic roughness length (z0eff) was estimated for the island of Taiwan by considering the individual roughness lengths (z0i) of the underlying LU types within a modeling grid box. Two z0eff datasets were prepared: one using the z0i from the default LU lookup table and the other using the observed z0i for three LU types (urban, dry cropland and pasture, and irrigated cropland and pasture). The spatial variability of the z0eff distribution was higher than that of the LU table-based z0 distribution. Three WRF sensitivity experiments were performed: (1) dominant LU table-based z0 (namely, S1), (2) z0eff estimated from the default z0i (namely, S2), and (3) z0eff estimated from the observed z0i (namely, S3). Comparisons of the thermal field, temperature, and surface sensible and latent heat fluxes revealed no significant differences among the three simulations. The wind field overestimation and surface momentum flux underestimation in S1 were reduced in S2 and S3, and these improvements were more prominent over areas with highly heterogeneous land surface conditions. Full article
(This article belongs to the Special Issue Meteorological Phenomena Driving Extreme Air Pollution)
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