Conversion of the BlueSky Framework into collaborative web service architecture and creation of a smoke modeling application

This project addresses the need for a collaborative architecture for scientific modeling that allows various scientific models to easily interact. The need for such a system has been documented by recent studies such as the JFSP Smoke Roundtables and the JFSP review of tools done by the Software Engineering Institute. This project addresses these needs by modifying the BlueSky Modeling Framework so that it can better serve as a collaborative architecture, and then utilizing this architecture to create an advanced application that could not otherwise be created. The BlueSky framework was modified for this purpose, and all changes integrated into all versions of BlueSky from 3.1.0 forward. BlueSky now contains a command line option that will automatically start it as a web-service provider, allowing it to be used by remote clients. When the web-service option is used, all models contained within BlueSky are automatically converted into web-service accessible modules, without need for a specialized web-service enabled version. Simple examples and documentation scripts designed to show a website or user interface creator how to access these models via web-service function calls were created. In addition, a more advanced website interface was created to show some of the advantages of web-service based scientific modeling. This tool, called BlueSky Playground, provides a single user interface into 10 models of fuels, consumption, emissions, plume rise, and smoke dispersion. A user can walk step-by-step through all of the model steps in the framework from fire information to smoke impact maps. At each step the user can choose the model they want to use and alter the modeled information before continuing on, allowing for a game-playing exploratory mode of interaction. Both the ability to access so many models through a single interface as well as the capability to obtain on-the-fly smoke dispersion calculations are novel to this tool. This application will be highlighted in 2010 through RX-410 classes as a way for users to learn about the various component models. It will also serve as a training tool for managers needing to run multiple scenarios and understand the implications of various choices. The web-service oriented architecture utilized in the project offers many potential advantages to scientific research done with the goal of decision support. Separation of the scientific computing portion of such work from the user interface allows scientists to focus on creating the best models and web designers to focus on creating the best interfaces. Remote functioning of the models through the web means that local installation of the model is no longer required solving distribution issues, and allows an Internet user to run a model that requires resources not available to them locally (such as large datasets or fast processors). Modularity allows for “mash-ups” where models are combined in ways not originally foreseen to meet emerging needs, and provides choices to be made on exact modeling pathways at the user or institutional level

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

Identification of Necessary Conditions for Arctic Transport of Smoke from United States Fires

The deposition of black carbon (BC), a dark absorbing aerosol, is a significant contributor to observed warming trends in the Arctic (Hansen and Nazarenko, 2004; Jacobson et al., 2007). Biomass burning outside of the Arctic, including wildland prescribed fires, is a major potential source of Arctic BC. Therefore, limiting or eliminating spring prescribed burning has been suggested to Congress as a BC reduction technique (e.g., Zender, 2007). However, there are large uncertainties in the current estimates of the sources, source regions, and transport and transformation pathways of BC transported to the Arctic region (Shindell et al., 2008; Hegg et al., 2009, Quinn et al., 2008). This study is the first comprehensive examination of the meteorological conditions required for emissions from the contiguous United States (CONUS) to be transported to Arctic. Using a simple trajectory modeling technique, we characterize the potential for transport of emissions from fires in CONUS to reach the Arctic and Greenland. The potential for Arctic transport is examined as • A 30-year climatology (1980-2009) of transport potential based on trajectory modeling using historical meteorology and split out by season, month, starting plume injection height, and time to reach the Arctic. • A real-time (daily) forecast system of transport potential to the Arctic that shows which layers of the atmosphere can reach the Arctic today, tomorrow, and the next day. The methods used here do not include wet or dry deposition and other factors that can further limit the ability of actual emissions to reach and deposit in the Arctic. Instead, by focusing on only one necessary aspect (a necessary but not sufficient condition) – the ability of the atmosphere to transport emissions – this study examines • Under what meteorological transport conditions can CONUS emissions potentially impact the Arctic? However, this allows for the ability to answer the corollary question: • Under what meteorological transport conditions will CONUS emissions not impact the Arctic? This inverse question allows for identification of times, locations, and plume injection heights where emissions sources (such as a prescribed burn) will not have an impact on the Arctic. This knowledge allows for both more targeted future studies and more precise mitigation strategies that do not focus on areas and times where Arctic impact is unlikely

PHASE 1 OF THE SMOKE AND EMISSIONS MODEL INTERCOMPARISON PROJECT (SEMIP): CREATION OF SEMIP AND EVALUATION OF CURRENT MODELS

Managers, regulators, and others often need information on the emissions from wildland fire and their expected smoke impacts. In order to create this information, combinations of models are utilized. The modeling steps follow a logical progression from fire activity through to emissions and dispersion. In general, several models and/or datasets are available for each modeling step, resulting in a large number of combinations that can be created to produce fire emissions or smoke impacts. Researchers, managers, and policy makers need information on how different model choices affect the resulting output, and guidance on what choices to make in selecting the models that best represent their management requirements. Baseline comparisons are needed between available models that highlight how they intercompare and, where possible, how their results compare with observations. As new models and methods are developed, standard protocols and comparison metrics are necessary to allow for these new systems to be understood in light of previous models and methods. The Smoke and Emissions Model Intercomparison Project (SEMIP) was designed to facilitate such comparisons. This project was designed to be the first step in a broader effort, and hence was titled Phase 1 of SEMIP. In Phase 1, SEMIP: • Examined the needs for fire emissions and smoke impact modeling; • Determined what data were available to help evaluate such models; • Identified a number of test cases that can serve as baseline comparisons between existing models and standard comparisons for new models; • Created a data warehouse and data sharing structure to help facilitate future comparisons; and • Performed a number of intercomparison analyses to examine existing models. SEMIP so far has resulted in: • Multiple peer reviewed journal articles and other documents; • Over 20 presentations; • Discussions with the EPA, JFSP, USFS F&AM, DOI, NWCG, and others on how to improve fire emissions calculations; • New fire emissions analysis tools; • Presentations and discussions with the JFSP on how to gather field observations useful to this type of analyses; and • Discussions with the JFSP on data sharing and archiving. SEMIP has also been acknowledged in recent RFAs from both the JFSP and NASA

An Evaluation of Modeled Plume Injection Height with Satellite-Derived Observed Plume Height

Plume injection height influences plume transport characteristics, such as range and potential for dilution. We evaluated plume injection height from a predictive wildland fire smoke transport model over the contiguous United States (U.S.) from 2006 to 2008 using satellite-derived information, including plume top heights from the Multi-angle Imaging SpectroRadiometer (MISR) Plume Height Climatology Project and aerosol vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). While significant geographic variability was found in the comparison between modeled plumes and satellite-detected plumes, modeled plume heights were lower overall. In the eastern U.S., satellite-detected and modeled plume heights were similar (median height 671 and 660 m respectively). Both satellite-derived and modeled plume injection heights were higher in the western U.S. (2345 and 1172 m, respectively). Comparisons of modeled plume injection height to satellite-derived plume height at the fire location (R2 = 0.1) were generally worse than comparisons done downwind of the fire (R2 = 0.22). This suggests that the exact injection height is not as important as placement of the plume in the correct transport layer for transport modeling

Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM_2.5 during wildfires. We estimated PM_2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R² of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM_2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM_2.5 concentrations during a major wildfire event

Assessing Satellite-Based Fire Data for use in the National Emissions Inventory

Biomass burning is significant to emission estimates because: (1) it can be a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite