An Interdisciplinary Approach to Coordinated Data Collection for Wildland Fire Science: the Fire and Smoke Model Evaluation Experiment (FASMEE)

Adam C. Watts, J. Morgan Varner, Elahe Soltanaghai, Leo Calle, Mohamad Alipour

Abstract: Coordination across multiple disciplines is necessary for current and future generations of modeling systems and the decision-support tools used for understanding and managing wildland fires. This coordination manifests in active-fire campaigns involving practitioners, modelers, and data-collection and management groups to produce datasets for training and evaluating models and underlying theories upon which they are built. New approaches and technology often are involved in these efforts, and in many cases their development is specified or driven by requirements identified by data-collection or model-evaluation activities. The Fire and Smoke Model Evaluation Experiment (FASMEE) is an effort to 1) conduct large-scale, active-fire data collection in order to produce a library of wildland fire model inputs 2) to advance wildland fire and smoke models, decision tools, and underlying science, and 3) to encourage disciplinary cross-training, diversity of backgrounds, and the development of new partnerships and technology in wildland fire science.

External IDs:dblp:conf/igarss/WattsVSCA24