[Eoas-seminar] [Seminar-announce] Scientific Computing Colloquium with Janice Coen

[eoas-seminar at lists.fsu.edu]

"Investigating wildland fire behavior through a computational science lens"

Janice Coen, Ph.D.
Senior Research Scientist
Dept of Environmental Science
University of San Francisco

NOTE: Please feel free to forward/share this invitation with other groups/disciplines that might be interested in this talk/topic. All are welcome to attend.

https://fsu.zoom.us/j/94273595552

Meeting # 942 7359 5552

Wednesday, Nov 2nd, 2022, Schedule:
* 3:00 to 3:30 PM Eastern Time (US and Canada)
Nespresso & Teatime (in 417 DSL Commons)

* 3:30 to 4:30 PM Eastern Time (US and Canada)
Colloquium - Attend F2F (in 499 DSL) or Virtually (via Zoom)

Abstract:
Investigating and predicting large wildland fire behavior is a longstanding research area that advanced rapidly in recent decades through expansion into an interdisciplinary field supported by computational models. The newer generation of models bidirectionally couple computational fluid dynamics models including weather prediction models with modules containing algorithms representing aspects of wildland fire behavior, simulating fire-atmosphere interactions across scales spanning three orders of magnitude. Integrated with airborne and satellite remote sensing data on wildland fuels and active fire detection, modern fire modeling systems have increased cost and complexity but are being used to address important societal problems. These include understanding how a few percent of ignitions produce exceptional wildfire events as well as destructive dynamical phenomena such as fire whirls, developing predictive systems for wildfire growth, and identifying hot spots of fine-scale extreme winds that may disrupt the electric grid and spark a rapidly spreading fire. Case studies of recent events illuminate both progress and limitations in our remote sensing systems, fire prediction tools, numerical weather prediction, and knowledge that add to wildfires’ mystery and apparent unpredictability.
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