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

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.

Role of the Madden‐Julian Oscillation in the Transport of Smoke From Sumatra to the Malay Peninsula During Severe Non‐El Niño Haze Events

“…Wind speeds, fire activity in Riau, and haze levels in Singapore all dropped significantly as the RMM left phase 7 on 23 June, returning to normal levels several days later when the RMM exited phase 8. This sequence of events is consistent with the diagnosis of Hertwig et al (), who attributed both the strong winds and the sudden increase in precipitation near Riau on 23 June to the presence of a tropical cyclone near Indonesia at this time. We return to this issue in section 3.5.…”

Public health impacts of the severe haze in Equatorial Asia in September–October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure

“…Finally, our approach relies on the assumption that the prevailing transport patterns between 2015 and 2006 are likely similar given the background meteorological conditions (sections 3.1-3.2). Predictive real-time tools to forecast haze in Southeast Asia exist and have recently been improved upon (Hertwig et al 2015), but these methods require substantial computational investment and do not readily provide the geographical source attribution information inherent in our adjoint approach.…”

Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997

“…This led us to significantly downscale the CO emissions over Sumatra, which we believe is likely associated with uncertainties in GFAS regarding the land cover type specification35. This adjustment significantly improved the agreement of the C-IFS-BG CO with MOPITT over the larger domain.…”