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.

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.

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.

[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.