The objective of this project is to improve wildland fire prediction and management by developing a deployable network of multistatic passive radars that can capture 3D wind patterns within smoke plumes. This cutting-edge system operates in the X-band and will provide the first-ever observations of wind dynamics inside smoke, offering new insights into fire behavior. By integrating advanced radar technology with high-resolution numerical weather prediction (NWP) models and machine learning techniques, the project will close critical knowledge gaps and enhance the ability to understand, predict, and respond to wildland fires.

This project plans to create a network of multistatic passive radars that will sample scattered signals from an active X-band radar source. Utilizing a multi-Doppler velocity retrieval algorithm, the project team aims to retrieve detailed 3D wind fields within smoke plumes. This technology builds on the previous success in deploying similar radars for thunderstorm dynamics research and will provide invaluable insights into the behavior of wildland fire smoke plumes. The high-resolution 3D wind observations are crucial for understanding the speed of smoke rise and subsequent transport. Integrating these observations with high-resolution NWP models will offer detailed information on smoke emissions, transport mechanisms, and their interactions with weather. This approach will improve smoke and air quality models, leveraging machine learning to conceptualize and parameterize plume rise phenomena. 

This project addresses a direct threat to military readiness by tackling the Department of War's (DoW) unique wildland fire challenges, which occur at the highest concentration among all federal lands due to essential ordnance, training, and testing activities. Unmanaged fire risks can shut down critical training ranges, damage multi-million-dollar infrastructure, and create dangerous conditions for personnel. By developing proactive fire management approaches tailored to military operations, this initiative prevents training disruptions while protecting valuable assets and reducing costly emergency response requirements. The research enables commanders to maintain continuous access to mission-essential training areas while advancing the state of fire monitoring technology, preserving DoW's ability to generate combat-ready forces. (Anticipated Project Completion - 2028)