The National Academies of Sciences, Engineering, and Medicine will appoint an ad hoc committee to examine how data collected from prescribed fires can be more effectively integrated and analyzed to improve wildland fire and smoke models.
During the course of its study, the committee will:
· Catalogue the breadth of research projects and data collection campaigns conducted by key agencies across the federal government (USFS, DOD, NASA, DOI, JFSP, NSF, NOAA, EPA) aimed at characterizing wildland fire and smoke. Identify the scientific goals, technical approaches, temporal and physical scope, data outputs, and methods of analysis of the campaigns and the elements of fire and smoke research that were most successfully addressed.
· Conduct consultations and facilitate discussions with experts in wildland fire research and management to identify key questions and challenges related to data collection, analysis, and modeling of wildland fire and smoke behavior, the capability of current models to capture relevant variables and interactions, other strengths and weaknesses of the models, and the extent to which knowledge gaps or other impediments to producing more robust models are understood.
· Identify opportunities for improving the efficiency of respective research efforts from federal agencies with the goal of improving wildland fire and smoke models. Such opportunities might arise, for example, through coordination of data collection, data sharing and integration, model evaluation and/or intercomparison, prioritization of efforts to resolve discrete information gaps, and strategic modifications of the research approach to resolve uncertainty and inform the refinement of models and characterization of uncertainty.
The committee will summarize the findings and conclusions of its study in a consensus report that describes how the output of research programs across the federal agencies can be more complementary and effective in informing and ultimately improving fire and smoke behavior models. The committee will recommend opportunities to improve coordinated data collection and analysis to improve model performance and prediction.
