Manuchehr Aminian, Cal Poly Pomona
On December 30, 2021, there was a devastating in the greater Boulder area in Colorado, as the result of a fire carried by intense (70-100mph) winds propagating what would normally be a localized grass fire, jumping into urban areas and destroying over one thousand homes and various other property. This was surprising to many - and was understood to be the result of a unique combination of weather and climate circumstances; see the following for a longer explanation:
High Winds and Marshall Fire on December 30th, 2021
In the wake of this event, our (fake) insurance company wants to re-evaluate overall fire risk for a variety of geographic regions which accounts for potential events as the result of similar weather patterns to the Marshall fire, through some combination of mathematical modeling and data analysis/data-driven methods.
Broadly, we have two main tasks for participants:
1. Identify existing mathematical models which are "expressive" enough to both capture the weather effects seen during the Marshall Fire, and also capture "nominal" weather. If they do not exist, construct one. Ideally, the relative frequency of these "intense" events can reflect reality to some degree. While we prefer a smaller, more useful model, an existing kitchen-sink, black-box weather simulation code which participants can successfully explain is acceptable.
2. A ranking, scoring, or flagging of a geographic regions throughout the US based on their relative risk for events such as the Marshall Fire. Any approach here is valid, as long as it is well-explained. Aside from "plugging in" the model in part 1, purely data-driven methods (e.g. data analysis; feature extraction/selection, outlier detection, anomaly detection, etc) are also welcome, but (again) explainability is important.
Read the final report