Development and demonstration of a Lagrangian dispersion modeling system for real‐time prediction of smoke haze pollution from biomass burning in Southeast Asia. Issue 24 (22nd December 2015)

• Record Type: Journal Article
• Authors: Hertwig, Denise; Burgin, Laura; Gan, Christopher; Hort, Matthew; Jones, Andrew; Shaw, Felicia; Witham, Claire; Zhang, Kathy
• Is Part Of: Journal of geophysical research. Volume 120:Issue 24(2015:Dec.)
• Page Start: 12605
• Page End: 12630
• Publication Date: 2015-12-22
• Subjects: biomass burning -- smoke haze -- Southeast Asia -- MODIS -- Lagrangian modeling -- bias correction; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1002/2015JD023422
• Languages: English
• ISSNs: 2169-897X

Abstract

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. Key Points: Lagrangian modeling of smoke haze dispersion with wildfire emission inventories Established confidence in use of modeling system as quantitative forecast tool Detailed representation of peat and cloud cover correction improves accuracy