Accessibility navigation


Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia

Hertwig, D., Burgin, L., Gan, C., Hort, M., Jones, A., Shaw, F., Witham, C. and Zhang, K. (2015) Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia. Journal of Geophysical Research: Atmospheres, 120 (24). pp. 12605-12630. ISSN 2169-8996

[img]
Preview
Text - Published Version
· Please see our End User Agreement before downloading.

6MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1002/2015JD023422

Abstract/Summary

Abstract Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satellite‐based active‐fire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including up‐to‐date representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a real‐time bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors.

Item Type:Article
Refereed:Yes
Divisions:No Reading authors. Back catalogue items
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:76901
Uncontrolled Keywords:biomass burning, smoke haze, Southeast Asia, MODIS, Lagrangian modeling, bias correction
Publisher:American Geophysical Union

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation