Search for: All records

Previous work has identified that one third of Americans live in an engineering education desert, or a county without a face-to-face 4-year accredited engineering degree or a two-year face-to-face pre-engineering program, regardless of whether it results in a credential. Iron Range Engineering (IRE) is an upper division engineering program designed to help provide access to individuals who due to financial and/or geographic location would be otherwise unable to participate in engineering. In this paper we present data demonstrating how this model of engineering education supports students from a range of geographical areas by addressing the research questions: Is IRE supporting students from engineering education deserts, are those students returning to their home region with a paid engineering position, and if so, with what income? In 2022, students at IRE were surveyed about their co-op experiences, feelings of belonging, and demographic information. This data was analyzed using descriptive statistics of location, salary, and demographic data. Our data supports the idea of this model of education attracting participation of students who otherwise may not have access to an engineering degree. Of the students in IRE, 67% come from engineering education deserts. Of those, 17 returned to their home state with a co-op and 8 returned to their home county with a paid co-op, at an average salary of $22/hour. Our results contribute to our understanding of engineering education deserts in the United States, and the IRE Bell co-op model as a curricular model to provide access to an affordable engineering degree for students in a wide range of locations. 

Abstract. Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The results are in good agreement with observations recorded during the event. Extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making and emergency response management during wildfire events. 

Abstract Lake surface conditions are critical for representing lake‐atmosphere interactions in numerical weather prediction. The Community Land Model's 1‐D lake component (CLM‐lake) is part of NOAA's High‐Resolution Rapid Refresh (HRRR) 3‐km weather/earth‐system model, which assumes that virtually all the two thousand lakes represented in CONUS have distinct (for each lake) but spatially uniform depth. To test the sensitivity of CLM‐lake to bathymetry, we ran CLM‐lake as a stand‐alone model for all of 2019 with two bathymetry data sets for 23 selected lakes: the first had default (uniform within each lake) bathymetry while the second used a new, spatially varying bathymetry. We validated simulated lake surface temperature (LST) with both remote and in situ observations to evaluate the skill of both runs and also intercompared modeled ice cover and evaporation. Though model skill varied considerably from lake to lake, using the new bathymetry resulted in marginal improvement over the default. The more important finding is the influence bathymetry has on modeled LST (i.e., differences between model simulations) where lake‐wide LST deviated as much as 10°C between simulations and individual grid cells experienced even greater departures. This demonstrates the sensitivity of surface conditions in atmospheric models to lake bathymetry. The new bathymetry also improved lake depths over the (often too deep) previous value assumed for unknown‐depth lakes. These results have significant implications for numerical weather prediction, especially in regions near large lakes where lake surface conditions often influence the state of the atmosphere via thermal regulation and lake effect precipitation. 

Abstract Injections of wildfire smoke plumes into the free troposphere impact air quality, yet model forecasts of injections are poor. Here, we use aircraft observations obtained during the 2019 western US wildfires (FIREX-AQ) to evaluate a commonly used smoke plume rise parameterization in two atmospheric chemistry-transport models (WRF-Chem and HRRR-Smoke). Observations show that smoke injections into the free troposphere occur in 35% of plumes, whereas the models forecast 59–95% indicating false injections in the simulations. False injections were associated with both models overestimating fire heat flux and terrain height, and with WRF-Chem underestimating planetary boundary layer height. We estimate that the radiant fraction of heat flux is 0.5 to 25 times larger in models than in observations, depending on fuel type. Model performance was substantially improved by using observed heat flux and boundary layer heights, confirming that models need accurate heat fluxes and boundary layer heights to correctly forecast plume injections. 

Abstract. Wildfire smoke is one of the most significant concerns ofhuman and environmental health, associated with its substantial impacts onair quality, weather, and climate. However, biomass burning emissions andsmoke remain among the largest sources of uncertainties in air qualityforecasts. In this study, we evaluate the smoke emissions and plumeforecasts from 12 state-of-the-art air quality forecasting systemsduring the Williams Flats fire in Washington State, US, August 2019, whichwas intensively observed during the Fire Influence on Regional to GlobalEnvironments and Air Quality (FIREX-AQ) field campaign. Model forecasts withlead times within 1 d are intercompared under the same framework basedon observations from multiple platforms to reveal their performanceregarding fire emissions, aerosol optical depth (AOD), surface PM2.5,plume injection, and surface PM2.5 to AOD ratio. The comparison ofsmoke organic carbon (OC) emissions suggests a large range of daily totalsamong the models, with a factor of 20 to 50. Limited representations of thediurnal patterns and day-to-day variations of emissions highlight the needto incorporate new methodologies to predict the temporal evolution andreduce uncertainty of smoke emission estimates. The evaluation of smoke AOD(sAOD) forecasts suggests overall underpredictions in both the magnitude andsmoke plume area for nearly all models, although the high-resolution modelshave a better representation of the fine-scale structures of smoke plumes.The models driven by fire radiativepower (FRP)-based fire emissions or assimilating satellite AODdata generally outperform the others. Additionally, limitations of thepersistence assumption used when predicting smoke emissions are revealed bysubstantial underpredictions of sAOD on 8 August 2019, mainly over thetransported smoke plumes, owing to the underestimated emissions on7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecastsshow both positive and negative overall biases for these models, with mostmembers presenting more considerable diurnal variations of sPM2.5.Overpredictions of sPM2.5 are found for the models driven by FRP-basedemissions during nighttime, suggesting the necessity to improve verticalemission allocation within and above the planetary boundary layer (PBL).Smoke injection heights are further evaluated using the NASA LangleyResearch Center's Differential Absorption High Spectral Resolution Lidar(DIAL-HSRL) data collected during the flight observations. As the firebecame stronger over 3–8 August, the plume height became deeper, with aday-to-day range of about 2–9 km a.g.l. However, narrower ranges arefound for all models, with a tendency of overpredicting the plume heights forthe shallower injection transects and underpredicting for the days showingdeeper injections. The misrepresented plume injection heights lead toinaccurate vertical plume allocations along the transects corresponding totransported smoke that is 1 d old. Discrepancies in model performance forsurface PM2.5 and AOD are further suggested by the evaluation of theirratio, which cannot be compensated for by solely adjusting the smoke emissionsbut are more attributable to model representations of plume injections,besides other possible factors including the evolution of PBL depths andaerosol optical property assumptions. By consolidating multiple forecastsystems, these results provide strategic insight on pathways to improvesmoke forecasts.