Statistical Comparison and Assessment of Four Fire Emissions Inventories for 2013 and a Large Wildfire in the Western United States

Wildland fires produce smoke plumes that impact air quality and human health. To understand the effects of wildland fire smoke on humans, the amount and composition of the smoke plume must be quantified. Using a fire emissions inventory is one way to determine the emissions rate and composition of smoke plumes from individual fires. There are multiple fire emissions inventories, and each uses a different method to estimate emissions. This paper presents a comparison of four emissions inventories and their products: Fire INventory from NCAR (FINN version 1.5), Global Fire Emissions Database (GFED version 4s), Missoula Fire Labs Emissions Inventory (MFLEI (250 m) and MFLEI (10 km) products), and Wildland Fire Emissions Inventory System (WFEIS (MODIS) and WFEIS (MTBS) products). The outputs from these inventories are compared directly. Because there are no validation datasets for fire emissions, the outlying points from the Bayesian models developed for each inventory were compared with visible images and fire radiative power (FRP) data from satellite remote sensing. This comparison provides a framework to check fire emissions inventory data against additional data by providing a set of days to investigate closely. Results indicate that FINN and GFED likely underestimate emissions, while the MFLEI products likely overestimate emissions. No fire emissions inventory matched the temporal distribution of emissions from an external FRP dataset. A discussion of the differences impacting the emissions estimates from the four fire emissions inventories is provided, including a qualitative comparison of the methods and inputs used by each inventory and the associated strengths and limitations

Keynote Talk: Leveraging the Edge-Cloud Continuum to Manage the Impact of Wildfires on Air Quality

International audienceThe emergence of large-scale cyberinfrastructure composed of heterogeneous computing capabilities and diverse sensors and other data sources are enabling new classes of dynamic data-driven "urgent" applications. However, as the variety of data sources, and the volume and velocity of data grow, processing this data while considering the uncertainty of infrastructure and timeliness constraints of urgent application workflows can be nontrivial and presents a new set of challenges. In this paper, we use an application workflow that monitors and manage the air quality impacts of remote wildfires to illustrate how the R-Pulsar programming system, leveraging the SAGE and WIFIRE platforms, can enable urgent analytics across the computing continuum. R-Pulsar supports urgent data-processing pipelines that tradeoff the content of data, cost of computation, and urgency of the results to support such workflows. We also discuss research challenges associated with programming urgent application workflows and managing resources in an autonomic manner