Prescribed fires in forest ecosystems can negatively impact human health and safety by transporting smoke downwind into nearby communities. Smoke transport to communities is known to occur around Bend, Oregon, United States of America (USA), where burning at the wildland–urban interface in the Deschutes National Forest resulted in smoke intrusions into populated areas. The number of suitable days for prescribed fires is limited due to the necessity for moderate weather conditions, as well as wind directions that do not carry smoke into Bend. To better understand the conditions leading to these intrusions and to assess predictions of smoke dispersion from prescribed fires, we collected data from an array of weather and particulate monitors over the autumn of 2014 and spring of 2015 and historical weather data from nearby remote automated weather stations (RAWS). We characterized the observed winds to compare with meteorological and smoke dispersion models using the BlueSky smoke modeling framework. The results from this study indicated that 1–6 days per month in the spring and 2–4 days per month in the fall met the general meteorological prescription parameters for conducting prescribed fires in the National Forest. Of those, 13% of days in the spring and 5% of days in the fall had “ideal” wind patterns, when north winds occurred during the day and south winds did not occur at night. The analysis of smoke intrusions demonstrated that dispersion modeling can be useful for anticipating the timing and location of smoke impacts, but substantial errors in wind speed and direction of the meteorological models can lead to mischaracterizations of intrusion events. Additionally, for the intrusion event modeled using a higher-resolution 1-km meteorological and dispersion model, we found improved predictions of both the timing and location of smoke delivery to Bend compared with the 4-km meteorological model. The 1-km-resolution model prediction fell within 1 h of the observed event, although with underpredicted concentrations, and demonstrated promise for high-resolution modeling in areas of complex terrain.

Smoke measurements were made during grass and forest understorey prescribed fires as part of a comprehensive programme to understand fire and smoke behaviour. Instruments deployed on the ground, airplane and tethered aerostat platforms characterised the smoke plumes through measurements of carbon dioxide (CO2), carbon monoxide (CO), methane (CH4) and particulate matter (PM), and measurements of optical properties. Distinctions were observed in aerial and ground-based measurements, with aerial measurements exhibiting smaller particle size distributions and PM emission factors, likely due to particle settling. Black carbon emission factors were similar for both burns and were highest during the initial flaming phase. On average, the particles from the forest fire were less light absorbing than those from the grass fires due to the longer duration of smouldering combustion in the forest biomass. CO and CH4 emission factors were over twice as high for the forest burn than for the grass burn, corresponding with a lower modified combustion efficiency and greater smouldering combustion. This dataset reveals the evolution of smoke emissions from two different commonly burned fuel types and demonstrates the complexity of emission factors.

Rorig, Miriam; Solomon, Robert; Krull, Candace; Peterson, Janice; Ruthford, Julia; Potter, Brian. 2013. Analysis of meteorological conditions for the Yakima Smoke Intrusion Case Study, 28 September 2009. Res. Pap. PNWRP-597. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 30 p. On 28 September 2009, the Naches Ranger District on the Okanogan-Wenatchee National Forest in south-central Washington state ignited an 800-ha prescribed fire. Later that afternoon, elevated PM2.5 concentrations and visible smoke were reported in Yakima, Washington, about 40 km east of the burn unit. The U.S. National Weather Service forecast for the day had predicted good dispersion conditions and winds that would carry the smoke to the less populated area north of Yakima. We undertook a case study of this event to determine whether conditions leading to the intrusion of the smoke plume into Yakima could have been predicted before the burn was ignited, either from forecasts and model output available on the day of the burn or from higher resolution model output made available only after the event. We evaluated three different meteorological model predictions: (1) 4-km resolution hourly weather predictions from the Weather Research and Forecasting (WRF) model that were available to forecasters on the day of the burn; (2) 4-km resolution WRF predictions at 10-minute intervals; and (3) 1.33-km resolution WRF predictions at 10-minute intervals. We found that predicted winds from the 4and 1.33-km model resolutions compared well with each other, whereas there were some differences in the predicted planetary boundary layer height over Yakima. We also used the high-resolution 1.33-km WRF output to generate smoke dispersion predictions using the BlueSky Smoke Modeling Framework. Results showed that forecasters and regulators using either the model output available on the day of the burn or the higher-resolution model output generated afterward, would not have anticipated the meteorological conditions that resulted in the smoke intrusion that day.

We evaluated predictions of hourly PM2.5surface 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 (>240μg/m3) 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.

PM2.5 surface concentrations were measured in smoke emitted by four wildfire events during fire seasons 2005–2008. These measurements fill a gap in the existing scientific PM2.5 observation database by providing a targeted wildfire-specific observation dataset. Four deployments occurred during various fire types including a managed-for-fuel-treatment wildfire complex, a wildfire complex, and two regional fire events. The maximum 24-h averaged values for each case were: 94.5μg/m3 (2005), 425μg/m3 (2006), 118μg/m3 (2007), and 247μg/m3 (2008). While these values are high, the diurnal concentration median and first quartile values remain below 35 and 10μg/m3, respectively. For all cases, the hourly diurnal patterns exhibit peak concentrations in the mid-morning and low concentrations in the mid-afternoon. Correlations between daily area actively burning and observed PM2.5 concentrations were significant for all cases and concentration patterns were found to be similar by geographic location rather than by type of fire (single vs. region-wide). Multiple co-located monitor types, the Environmental Proof Beta Attenuation Monitor, which measures PM2.5 concentrations using a beta-beam aimed at particulates collected on filter tape, and the E-SAMPLER and DataRAM, which both use nephelometry to measure PM2.5 concentrations, showed statistically good agreement.

Smoke from fire is a local, regional and often international issue that is growing in complexity as competition for airshed resources increases. BlueSky is a smoke modeling framework designed to help address this problem by enabling simulations of the cumulative smoke impacts from fires (prescribed, wildland, and agricultural) across a region. Versions of BlueSky have been implemented in prediction systems across the contiguous US, and land managers, air-quality regulators, incident command teams, and the general public can currently obtain BlueSky-based predictions of smoke impacts for their region. A highly modular framework, BlueSky links together a variety of state-of-the-art models of meteorology, fuels, consumption, emissions, and air quality, and offers multiple model choices at each modeling step. This modularity also allows direct comparison between similar component models. This paper presents the overall model framework Version 2.5 - the component models, how they are linked together, and the results from case studies of two wildfires. Predicted results are affected by the specific choice of modeling pathway. With the pathway chosen, the modeled output generally compares well with plume shape and extent as observed by satellites, but underpredicts surface concentrations as observed by ground monitors. Sensitivity studies show that knowledge of fire behavior can greatly improve the accuracy of these smoke impact calculations.

Dry thunderstorms (those that occur without significant rainfall at the ground) are common in the interior western United States. Moisture drawn into the area from the Gulfs of Mexico and California is sufficient to form high-based thunderstorms. Rain often evaporates before reaching the ground, and cloud-to-ground lightning generated by these storms strikes dry fuels. Fire weather forecasters at the National Weather Service and the National Interagency Coordination Center try to anticipate days with widespread dry thunderstorms because they result in multiple fire ignitions, often in remote areas. The probability of the occurrence of dry thunderstorms that produce fire-igniting lightning strikes was found to be greater on days with high instability and a deficit of moisture at low levels of the atmosphere. Based on these upper-air variables, an algorithm was developed to estimate the potential of dry lightning (lightning that strikes the ground with little or no rainfall at the surface) when convective storms are expected. In the current study, this algorithm has been applied throughout the western United States, with modeled meteorological variables rather than the observed soundings that have previously been used, to develop a predictive scheme for estimating the risk of dry thunderstorms. Predictions of the risk of dry thunderstorms were generated from real-time forecasts using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) for the summers of 2004 and 2005. During that period, 240 large lightning-caused fires were ignited in the model domain. Of those fires, 40% occurred where the probability of dry lightning was predicted to be equal to or greater than 90% and 58% occurred where the probability was 75% or greater.

Abstract,, Sue A.; Rorig, Miriam L. 2003. Regional pollution potential in the Northwestern United States. Gen. Tech. Rep. PNW-GTR-590. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 26 p. The potential for air pollution from industrial sources,to reach wilderness,areas

Case study analyses of the BlueSky smoke modeling framework help identify the input values or modeling components that require improvement. BlueSky is a smoke modeling forecasting system that combines burn information with models of consumption, emissions, meteorology, and dispersion to yield a prediction of surface concentrations of particulate matter of diameter less than 2.5 micrometers (PM2.5) and of diameter less than 10 micrometers (PM10) from wildland fire. For additional information regarding the BlueSky smoke modeling framework, see O’Neill et al (2003) in this issue (J8.7). In this work BlueSky has been applied to several wildfires to provide a thorough analysis of system performance. Case studies include the Bitterroot Wildfire Complex of 2000 in Montana and Idaho and the Hayman Fire of 2002 in Colorado. Deficiencies discovered in individual model components during the course of these case studies will be fixed and improvements will be incorporated into the real-time BlueSky smoke modeling system. A drought over the West intensified over the spring and early summer of 2000 when a persistent upper-level ridge parked over the Northern Rockies sent fuel moistures plummeting. Monsoonal moisture from the south caused thunderstorms that produced “dry lightning,” which triggered wildfires across the Intermountain West. Many of these fires persisted from June to October and joined with others to become huge wildfire complexes. Approximately 250 of these wildfire complexes burned in the Rocky Mountains of Idaho and Montana, centering on the Bitterroot mountain range on the border of the two states. Figure 1 displays a map of many of the fires simulated by BlueSky within the case study domain.

Abstract A large number of lightning-caused fires burned across the western United States during the summer of 2000. In a previous study, the authors determined that a simple index of low-level moisture (85-kPa dewpoint depression) and instability (85–50-kPa temperature difference) from the Spokane, Washington, upper-air soundings was very useful for indicating the likelihood of “dry” lightning (occurring without significant concurrent rainfall) in the Pacific Northwest. This same method was applied to the summer-2000 fire season in the Pacific Northwest and northern Rockies. The mean 85-kPa dewpoint depression at Spokane from 1 May through 20 September was 17.7°C on days when lightning-caused fires occurred and was 12.3°C on days with no lightning-caused fires. Likewise, the mean temperature difference between 85 and 50 kPa was 31.3°C on lightning-fire days, as compared with 28.9°C on non-lightning-fire days. The number of lightning-caused fires corresponded more closely to high instability and high dewp...

Lightning is the primary cause of fire in the forested regions of the Pacific Northwest, especially when it occurs without significant precipitation at the surface. Using thunderstorm occurrence and precipitation observations for the period 1948‐77, along with automated lightning strike data for the period 1986‐96, it was possible to classify convective days as either ‘‘dry’’ or ‘‘wet’’ for several stations in the Pacific Northwest. Based on the classification, a discriminant analysis was performed on coincident upper-air sounding data from Spokane, Washington. It was found that a discriminant rule using the dewpoint depression at 85 kPa and the temperature difference between 85 and 50 kPa was able to classify correctly between 56% and 80% of the convective days as dry or wet. Also, composite maps of upper-air data showed distinctly different synoptic patterns among dry days, wet days, and all days. These findings potentially can be used by resource managers to gain a greater understanding of the atmospheric conditions that are conducive to lightning-induced fires in the Pacific Northwest.