Tuesday, 9 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
Barbara G. Brown, NCAR, Boulder, CO; and B. Kosovic, P. A. Jimenez, D. Munoz Esparza, B. Schmidt, A. R. S. Anderson, J. Cowie, K. Sampson, J. Boehnert, and A. DeCastro
In response to numerous large and destructive wildfires in the State of Colorado, the Colorado State Legislature in 2015 enacted a bill to establish and support development of a Colorado Fire Prediction System (CO-FPS) that will provide an enhanced capability to predict and manage wildfires. As a result of this bill, and in collaboration with the Colorado Division of Fire Prevention and Control (DFPC) and their Center of Excellence (CoE) for Advanced Technology Aerial Firefighting, NCAR is iteratively developing a decision support system to meet end-users’ needs for fire information. The central component of the system is a coupled fire-weather forecasting model (WRF-Fire) based on the CAWFE (Coupled Atmosphere-Wildland Fire Environment) model developed by Coen (2013). CO-FPS is designed to be utilized by fire managers to predict the likely characteristics of active wildfires – for example, rate-of-spread, predicted fire perimeter – as well as other user-relevant variables such as flame length, smoke dispersion and the existence of significant events such as strong wind gusts or pyrocumulus.
The CO-FPS system is being developed and tested with close consideration and understanding of needs by Colorado stakeholders. In particular, CO-FPS development is being guided by significant ongoing interactions with the Colorado DFPC and other stakeholders (e.g., air quality managers, representatives of local fire-fighting organizations). Experimental fire simulations are available to these stakeholders and others in the fire and weather forecasting communities in near-real-time via the Colorado Wildfire Information Management System, with fire perimeters, weather information, and other phenomena published via a GIS platform and presented through a graphical user interface that allows a variety of queries by users. The system is expected to be operational in 2020.
Multiple challenges faced in development of this system (e.g., limited availability of observations for evaluation of some phenomena, communication of needs and system capabilities) will be addressed in this presentation. In addition, lessons learned via the interactions and collaborations that are inherent in development of a useful and successful system will be considered.
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