Had you told me 3 years ago that in 2016 I was going to be riding in the passenger’s seat to help Colorado’s experts in wildland firefighting get their hands on a new fire prediction model, I would have kind of squinted my eyes at you (that’s my puzzled look, not angry) and said, “Come again?"
For that matter, you could have said that to me in January of this year and I might have had the same response—that’s just how quickly new, cool data integrations heat up at Intterra.
We recently enjoyed a significant milestone in our joint venture with the Colorado Division of Fire Prevention and Control (DFPC) and the National Center for Atmospheric Research (NCAR) to add the latest exploration into the CAWFE® model for fire prediction. That’s CAWFE®, sounds a lot like “coffee” with emphasis on the front-end “caw." After years of research and development, NCAR and DFPC unveiled the Colorado Fire Prediction System to a group of trusted users via SituationAnalyst (SA).
A New Fire Prediction System for Firefighters
From my seat, it’s a brand new data set that's now easily and instantaneously in the hands of the people who need it most—our firefighters and incident teams on the ground, wrestling to gain control of a fire. Whether they love it, fear it, or just plain need to get their arms around it, the data is available to them in a way it wasn't before. The prediction model that NCAR has been feverishly working on with our Colorado partners takes into account a starting point of the fire, any existing fire barriers entered into SA, and the weather & atmospheric conditions along with terrain and fuels of the area. The CAWFE® simulation returns a prediction model that can span 18 hours of anticipated fire behavior.
We have been curious observers as this has been unfurling in front of us, listening for how to make the user experience seamless, while our users' minds churn on the data presented to them.
SA helps break it down for the users by enabling animation of the model, providing the ability to focus on one particular attribute or time frame of the prediction, and allowing additional data to be layered on top of it. The State of Colorado has invested tremendously in their acquisition of data to aid in the prevention and quick elimination of wildfires. The way that SA assists in the preservation of Colorado's beloved natural resources through planning, protecting, defending, and now, even predicting, is pretty powerful.
Seamless Information from the Fireline to the Model and Back
What was our job in the process? Namely, serving the data up efficiently, nimbly, and in a way that is readily usable. That’s one of the foundational tenets of the ever-continuing improvements in SA. In the last few weeks, we reached a milestone with NCAR and the State of Colorado. The image above shows a team of wildfire and emergency management experts from across the state getting their hands on this pivotal beta information for the first time. I can glean a fraction of what this team in the picture can from the map, and I anticipate that this is just one of many amazing developments SA is capable of.
As I have made the leap from financial services/project management/consulting/momming (in no particular order) to this niche of serving up emergency services analytics on the map, my mind ponders what else we might be able to bring to life that helps business owners, agency representatives, strategists and ecological warriors make decisions?
Bringing this full-circle - take the leap - my outlook on the power of geospatial data is that all managers, people of influence, decision-makers, and tactical operators need to have the capability to visualize their data, efficiently and effectively - so, I'm curious, who's next? Who has some fresh data for us to help visualize?
Want more information on NCAR's fire prediction work? https://www2.ucar.edu/
