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

Abstract

[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.

The role of semi-volatile organic compounds in the mesoscale evolution of biomass burning aerosol: a modeling case study of the 2010 mega-fire event in Russia

“…Thus our results indicate that the use of the conventional OA modeling methods in studies of BB aerosol mesoscale evolution can result in considerable negative biases in the simulated aerosol concentrations; probably, such biases can explain at least a part of the earlier reported systematic discrepancies between BB aerosol concentrations from modeling and measurements (Wang et al, 2006;Strand et al, 2012). Note that, in general, our findings concerning potential deficiencies of the "conventional" approach to OA modeling are in line with the findings of several earlier studies (e.g., Heald et al, 2005;Bessagnet et al, 2009;Hodzic et al, 2010;Zhang et al, 2013) in which chemistry transport models considerably underestimated observed concentrations of OA originating from various sources when using the conventional approach.…”

“…BB emissions are also known to be a major source of particulate organic matter (POM), which contributes to both direct and indirect radiative forcing by providing absorbing brown carbon (e.g., Chakrabarty et al, 2010;Saleh et al, 2014), enhancing light absorption by BC (up to a factor of 2) due to the lensing effect (Jacobson, 2001), as well as contributing to the light scattering (Keil and Haywood, 2003). Episodes of a major impact of aerosol emissions from fires on regional air quality have been reported worldwide (e.g., Heil and Goldammer, 2001;Andreae et al, 2002;Sinha et al, 2003;Bertschi and Jaffe, 2005;Konovalov et al, 2011;Strand et al, 2012;Andreae et al, 2012;Engling et al, 2014). Therefore, the physical and chemical properties of BB aerosol and its sources and evolution have to be adequately represented in atmospheric numerical models aimed at analyzing and predicting climate change and air pollution phenomena (e.g., Kiehl et al, 2007;Goodrick et al, 2012).…”

Four-dimensional variational inversion of black carbon emissions during ARCTAS-CARB with WRFDA-Chem

“…Among other conclusions, those authors found that at six surface sites near the land-ocean boundary, 4 and 12 km simulations with similar settings had mean wind speed biases of (0.15 to 1.5) m s −1 and (−0.38 to 1.9) m s −1 , respectively. Supporting that conclusion, Strand et al (2012) used a 36 km resolution chemical transport model (CTM), with offline meteorology, and found significant negative mean fractional bias (MFB) in modeled PM 2.5 relative to surface observations of fires within narrow northern California valleys in July 2008 (MFB = −34.95 %) and during autumn 2007 Santa Ana winds (MFB = −110.22 %). During the July 2008 episode, their CTM predictions had a smaller positive bias (MFB = +21.88 %).…”

“…Temporal averaging of the observations will not necessarily solve that problem since perfectly modeled transport could still send a mislocated source in an entirely different direction than the truthfully located source. This effect is evident for valley fires (Strand et al, 2012), since placing the sources in the basin or spreading them throughout the basin and the peaks will result in different "downwind" concentrations. Downwind might be a very different direction if the convective-scale winds contribute more information than the mesoscale winds to the true source-receptor relationship.…”

Particulate Matter, Ozone, and Nitrogen Species in Aged Wildfire Plumes Observed at the Mount Bachelor Observatory

“…For example, Strand et al (2012) found that the BlueSky system predicted PM 2.5 concentrations well for some of the [2007][2008] Californian fires the authors analyzed, but was biased low for others. Herron- Thorpe et al (2014) found that the Air Information Report for Public Access and Community Tracking (AIRPACT) version-3 framework captured the location and transport direction of fire plumes from western US wildfires occurring during [2007][2008], but generally underestimated PM 2.5 concentrations.…”