Wind-blown dust modeling using a backward Lagrangian particle dispersion model

Wind-blown dust modeling using a backward Lagrangian particle dispersion model AbstractPresented here is a new dust modeling framework that uses a backward Lagrangian particle dispersion model coupled with a dust emission model, both driven by meteorological data from the Weather Research and Forecasting model (WRF). The modeling framework’s performance was tested during the spring of 2010 at multiple sites across northern Utah. Initial model results for March-April 2010 showed that the model was able to replicate the 27-28 April 2010 dust event; however, it was unable to reproduce a significant wind-blown dust event on 30 March 2010. During this event, the model significantly underestimated observed PM2.5 concentrations (4.7 μg m−3 vs. 38.7 μg m−3) along the Wasatch Front. The backward Lagrangian approach presented here allowed for the easy identification of dust source regions with misrepresented land cover and soil types, which required an update to WRF. In addition, changes were also applied to the dust emission model to account for dry lake basins. These updates significantly improved dust model simulations with modeled PM2.5 comparing much more favorably to observed PM2.5 concentrations (average of 30.3 μg m−3) in addition to better resolving the timing of the frontal passage. The dust model was also applied in a forecasting setting, with the model able to replicate the magnitude of a large dust event, albeit with a 2-hour lag. These results suggest that the dust modeling framework presented here has potential for replicating past dust events, identifying potential source regions of dust, and short-term forecasting applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Meteorology and Climatology American Meteorological Society

Wind-blown dust modeling using a backward Lagrangian particle dispersion model

Loading next page...
 
/lp/ams/wind-blown-dust-modeling-using-a-backward-lagrangian-particle-1B5g053AXs
Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1558-8432
D.O.I.
10.1175/JAMC-D-16-0351.1
Publisher site
See Article on Publisher Site

Abstract

AbstractPresented here is a new dust modeling framework that uses a backward Lagrangian particle dispersion model coupled with a dust emission model, both driven by meteorological data from the Weather Research and Forecasting model (WRF). The modeling framework’s performance was tested during the spring of 2010 at multiple sites across northern Utah. Initial model results for March-April 2010 showed that the model was able to replicate the 27-28 April 2010 dust event; however, it was unable to reproduce a significant wind-blown dust event on 30 March 2010. During this event, the model significantly underestimated observed PM2.5 concentrations (4.7 μg m−3 vs. 38.7 μg m−3) along the Wasatch Front. The backward Lagrangian approach presented here allowed for the easy identification of dust source regions with misrepresented land cover and soil types, which required an update to WRF. In addition, changes were also applied to the dust emission model to account for dry lake basins. These updates significantly improved dust model simulations with modeled PM2.5 comparing much more favorably to observed PM2.5 concentrations (average of 30.3 μg m−3) in addition to better resolving the timing of the frontal passage. The dust model was also applied in a forecasting setting, with the model able to replicate the magnitude of a large dust event, albeit with a 2-hour lag. These results suggest that the dust modeling framework presented here has potential for replicating past dust events, identifying potential source regions of dust, and short-term forecasting applications.

Journal

Journal of Applied Meteorology and ClimatologyAmerican Meteorological Society

Published: Sep 1, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off