Chapter 22 Regional Real-Time Smoke Prediction Systems

O’Neill, Susan; Larkin, Narasimhan K.; Hoadley, Jeanne; Mills, Graham A.; Vaughan, J. K.; Draxler, Roland R.; Rolph, Glenn D.; Ruminski, M.; Ferguson, Sue A.

doi:10.1016/s1474-8177(08)00022-3

Cited by 8 publications

(7 citation statements)

References 19 publications

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“…These data sets have inherent error because of their “real‐time” nature; they are not nudged with data assimilation nor are they corrected before being used to calculate emissions and surface concentrations from wildfire(s). While error exists in daily predictions without large episodic events, daily prediction‐error will always exist for cases of wildfire smoke because the information required and used is in nearly raw form [ O'Neill et al , 2008]. For example, AIRPACT‐3, a regional (U.S.A. Pacific Northwest) real‐time prediction system, produced daily predicted 24 h surface PM 2.5 concentrations with a MFB of 3% and a larger MFE of 58% [ Chen et al , 2008] when wildfire PM 2.5 was present during one month of a four‐month analysis period.…”

Section: Discussion Of Errormentioning

confidence: 99%

“…These warnings are based on predictions made by modeling systems that simulate the wildfire source(s) and emissions as well as smoke plume transport, dispersion, and subsequent surface concentrations. Efforts to predict daily PM 2.5 concentrations from prescription burning or wildfires are ongoing around the world [e.g., O'Neill et al , 2008]. For example, the Fire Locating and Modeling of Burning Emissions (FLAMBÉ), a system that uses satellite‐detected fires and simple emission algorithms to predict smoke dispersion on a global domain, has proven effective for determining smoke transport pathways from Amazonia fires [ Reid et al , 2004; Wang et al , 2006].…”

Section: Introductionmentioning

confidence: 99%

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Analyses of BlueSky Gateway PM_2.5 predictions during the 2007 southern and 2008 northern California fires

et al. 2012

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[1] We evaluated predictions of hourly PM 2.5 surface concentrations produced by the experimental BlueSky Gateway air quality modeling system during two wildfire episodes in southern California (Case 1) and northern California (Case 2). In southern California, the prediction performance was dominated by the prevailing synoptic weather patterns, which differentiated the smoke plumes into two types: narrow and highly concentrated during an offshore flow, and diluted and well-mixed during a light onshore flow. For the northern California fires, the prediction performance was dominated by terrain and the limitations of predicting concentrations in a narrow valley, rather than by the synoptic pattern, which did not differ much throughout the wildfire episode. There was an over-prediction bias for the maximum values during this episode. When the predicted values were compared to observed values, the best performance results were for the onshore flow during the southern California fires, indicating that the coarse grid used by BlueSky Gateway appropriately represented these well-mixed conditions. Overall, the southern California fire predictions were biased low and the model did not reproduce the high hourly concentrations (>240 mg/m 3 ) observed by the monitors. The predicted results performed well against the observations for the northern California fires, with a large number of predicted values within acceptable range of the observed values.Citation: Strand, T.

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Section: Discussion Of Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyses of BlueSky Gateway PM_2.5 predictions during the 2007 southern and 2008 northern California fires

et al. 2012

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“…For wildfire smoke emissions, fire points and smoke plume locations were identified by the NOAA Hazard Mapping System (HMS) using satellite retrieval and human analysis (Ruminski et al, 2006). The HMS fire smoke products were processed through the U.S. Forest Service BlueSky (version 3.1) framework modelling system O'Neill et al, 2009) to produce near real-time wildfire smoke emissions for the simulations.…”

Section: Air Quality Model and Domainmentioning

confidence: 99%

The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?

Saylor

Baker

Lee

et al. 2019

Tellus B: Chemical and Physical Meteorology

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2019) The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?, Tellus B: Chemical and Physical Meteorology, 71:1, 1550324, ABSTRACT Dry deposition is an important loss process for atmospheric particles and can be a significant part of total deposition estimates calculated for critical loads analyses. However, algorithms used in large-scale air quality and atmospheric chemistry models to predict particle deposition velocity as a function of particle size are highly uncertain. Many of these algorithms, although derived from a common heritage, predict vastly different particle deposition velocities for a given particle diameter even under identical environmental conditions for major land use classes. Even more problematic, for vegetated landscapes (forests, in particular) the algorithms do not agree very well with available measurements. In this work, we perform a sensitivity study to estimate how significant the uncertainties in particle deposition algorithms may be in an air quality model's predictions of ground-level fine particle concentrations, particle deposition and overall total deposition of nitrogen and sulfur. Our results suggest that fine particle concentration predictions at the surface may vary by 5-15% depending on the choice of particle deposition velocity algorithm, while particle dry deposition is affected to a much greater extent with differences among algorithms >200%. Moreover, if accumulation mode particle dry deposition measurements over forests are correct, then dry particle deposition and total elemental deposition to these landscapes may be much larger than is typically simulated by current air quality and atmospheric chemistry models, calling into question commonly available estimates of total deposition and their use in critical loads analyses. Since accurate predictions of atmospheric particle concentrations and deposition are critically important for future air quality, weather and climate models and management of pollutant deposition to sensitive ecosystems, an investment in new dry deposition measurements in conjunction with integrated modelling efforts seems not only justified but vitally necessary to advance and improve the treatment of particle dry deposition processes in atmospheric models.

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“…The ClearSky prediction system (http://www.clearsky.wsu.edu) was developed to forecast smoke dispersion, especially from agriculture-related fires in the U.S. Pacific Northwest. In Australia, the Australian Bureau of Meteorology's smoke prediction system was developed to assist land managers in planning prescribed burnings while reducing the impacts of smoke from these fires [24,25].…”

Section: Introductionmentioning

confidence: 99%

Development of a Smoke Dispersion Forecast System for Korean Forest Fires

Lee

Cho

Lee³

et al. 2019

Forests

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Smoke from forest fires is a growing concern in Korea as forest structures have changed and become more vulnerable to fires associated with climate change. In this study, we developed a Korean forest fire smoke dispersion prediction (KFSDP) system to support smoke management in Korea. The KFSDP system integrates modules from different models, including a Korean forest fire growth prediction model, grid-based geographic information system (GIS) fuel loading and consumption maps generated by national forest fuel inventory data, and the Korean Weather Research and Forecasting Model, into a Gaussian plume model to simulate local- and regional-scale smoke dispersion. The forecast system is operated using grid-based fires and simulates a cumulative smoke dispersion of carbon monoxide (CO) and <2.5 µm and <10 µm particulate matter (PM2.5 and PM10, respectively) ground-level concentration contours at 30-min intervals during the fire in concert with weather forecasts. The simulated smoke dispersions were evaluated and agreed well with observed smoke spreads obtained from real forest fires in Korea, and the performance of the KFSDP system was also analyzed using “what-if” scenarios. This is the first study to develop an integrated model for predicting smoke dispersion from forest fires in Korea.

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Chapter 22 Regional Real-Time Smoke Prediction Systems

Cited by 8 publications

References 19 publications

Analyses of BlueSky Gateway PM_2.5 predictions during the 2007 southern and 2008 northern California fires

Analyses of BlueSky Gateway PM_2.5 predictions during the 2007 southern and 2008 northern California fires

The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?

Development of a Smoke Dispersion Forecast System for Korean Forest Fires

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Chapter 22 Regional Real-Time Smoke Prediction Systems

Cited by 8 publications

References 19 publications

Analyses of BlueSky Gateway PM2.5 predictions during the 2007 southern and 2008 northern California fires

Analyses of BlueSky Gateway PM2.5 predictions during the 2007 southern and 2008 northern California fires

The particle dry deposition component of total deposition from air quality models: right, wrong or uncertain?

Development of a Smoke Dispersion Forecast System for Korean Forest Fires

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Product

Resources

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Analyses of BlueSky Gateway PM_2.5 predictions during the 2007 southern and 2008 northern California fires

Analyses of BlueSky Gateway PM_2.5 predictions during the 2007 southern and 2008 northern California fires