Proof-of-Concept Mesoscale Discussions for Short-Term Wildfire Prediction on the Southern Great Plains

Particularly damaging wildfires synthesize anomalous environments characterized by short-lived combinations of vegetative fuel and weather. Mesoscale atmospheric processes and their juxtaposition with high-risk fuels greatly influence the potential for dangerous fire behavior and spread. Such atmospheric effects on the fire environment are well suited to the application of skillful meteorological mesoanalysis, including short-term predictions aided and informed through the use of Convection-allowing Models (CAMs). This poster will highlight efforts by the Southern Great Plains Wildfire Outbreak Working Group, an online collaborative multi-agency operations-to-research-to-operations community focused on science-based support to state forestry agencies in Texas, Oklahoma, and Kansas, to provide proof-of-concept mesoscale messaging to inform tactical decision making for wildland fire management officials.

Analogous to Mesoscale Discussions (MDs) issued by the Storm Prediction Center for evolving convective hazards, the group posted a total of 43 fire-focused MDs during the 2021/22 southern Great Plains fire season. The fire MDs were based on detailed analyses spanning the meso-alpha and meso-beta scales using trends in remote sensing and in situ observational data and the evolution of fire-effective atmospheric features depicted in CAMs relative to fuel environments. In many cases, fire MDs successfully predicted the onset of problematic wildfire occurrence on the sub-county warning area scale hours prior to ignition. In other instances, life threatening escalations in fire behavior were highlighted prior to the onset of particularly dangerous fire conditions, including in advance of wildfire/wind shift interactions. This information improved situational awareness of imminent fire impacts and informed mitigative actions and services by fire analysts and meteorologists alike for problematic and significant wildfires.