A METHODOLOGY FOR THE CREATION OF METEOROLOGICAL DATASETS FOR LOCAL AIR QUALITY MODELLING AT AIRPORTS
Item
Title (Dublin Core)
eng
A METHODOLOGY FOR THE CREATION OF METEOROLOGICAL DATASETS FOR LOCAL AIR QUALITY MODELLING AT AIRPORTS
Description (Dublin Core)
eng
In order to properly estimate local air pollution concentrations at airports, several different dispersion models are
routinely applied using a variety of different modeling approaches (Gaussian, Lagrangian or Eulerian). Common to all dispersion
models is the requirement for accurate meteorological parameters. The paper outlines the benefits and risks of three separate
approaches to obtain meteorological input for atmospheric dispersion models.
The preferred approach is based on directly-measured observations and the primary source of readily available observed data at
airports is METAR. A typical METAR report contains observations of temperature, dew point, wind, precipitation, cloud cover,
cloud heights, visibility, and barometric pressure. However, most dispersion models require information on atmospheric stability.
Although stability is not directly reported in METAR data, a widely-available algorithm allows for the estimation of atmospheric
stability class using measured values of wind speed and the observed cloud cover.
The next preferred option should be used when METAR or other observed data are not readily available or more sophisticated 3D
gridded meteorological fields are required by the specific dispersion model. This second approach uses meso-scale numerical
weather prediction (NWP) models. These models can produce high quality ‘best guess’ meteorological fields on a wide variety of
time and distance scales. NWP models, however, require high-level meteorological expertise in order to run and are computationally
intense. This may make the NWP approach impractical for use in routine applications or for large-scale studies which involve many
different airports.
Finally, this paper outlines a third approach to obtaining meteorological data. This approach uses long-term, globally-archived,
gridded meteorological analysis fields, such as REANALYSIS data, which are readily available and cover long-term time scales.
Although less accurate than METAR and NWP models, this approach may be of benefit to those users who require ‘good guess’
meteorological data for air pollution studies in those cases where direct observations, such as METAR, are not available and NWP
modelling is not a viable solution.
routinely applied using a variety of different modeling approaches (Gaussian, Lagrangian or Eulerian). Common to all dispersion
models is the requirement for accurate meteorological parameters. The paper outlines the benefits and risks of three separate
approaches to obtain meteorological input for atmospheric dispersion models.
The preferred approach is based on directly-measured observations and the primary source of readily available observed data at
airports is METAR. A typical METAR report contains observations of temperature, dew point, wind, precipitation, cloud cover,
cloud heights, visibility, and barometric pressure. However, most dispersion models require information on atmospheric stability.
Although stability is not directly reported in METAR data, a widely-available algorithm allows for the estimation of atmospheric
stability class using measured values of wind speed and the observed cloud cover.
The next preferred option should be used when METAR or other observed data are not readily available or more sophisticated 3D
gridded meteorological fields are required by the specific dispersion model. This second approach uses meso-scale numerical
weather prediction (NWP) models. These models can produce high quality ‘best guess’ meteorological fields on a wide variety of
time and distance scales. NWP models, however, require high-level meteorological expertise in order to run and are computationally
intense. This may make the NWP approach impractical for use in routine applications or for large-scale studies which involve many
different airports.
Finally, this paper outlines a third approach to obtaining meteorological data. This approach uses long-term, globally-archived,
gridded meteorological analysis fields, such as REANALYSIS data, which are readily available and cover long-term time scales.
Although less accurate than METAR and NWP models, this approach may be of benefit to those users who require ‘good guess’
meteorological data for air pollution studies in those cases where direct observations, such as METAR, are not available and NWP
modelling is not a viable solution.
Creator (Dublin Core)
Duchene, Nicolas
Smith, James
Fuller, Ian
Subject (Dublin Core)
eng
METAR;REANALYSIS;MM5;WRF;numerical weather prediction;local air quality;dispersion;stability;data completion
Publisher (Dublin Core)
Croatian meteorological society
Date (Dublin Core)
2008
Type (Dublin Core)
text
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
https://hrcak.srce.hr/64261
eng
https://hrcak.srce.hr/file/96378
Source (Dublin Core)
Hrvatski meteorološki časopis
ISSN 1330-0083 (Print)
ISSN 1849-0700 (Online)
Volume 43
Issue 43/1
Language (Dublin Core)
eng
Rights (Dublin Core)
info:eu-repo/semantics/openAccess
The papers of this Journal are free of charge for personal or educational use, with respect of copyright of authors and publisher.