Snow Days, Shovels, and the Math of Melting: How a Winter Storm Becomes a STEM Classroom
- James Purse
- 7 days ago
- 4 min read
By, James “Jimi” Purse
Arcadia Education Partners
Partnering with schools to align leadership, communication, and innovation. inspiredbyarcadia.com
A few days ago, I was buried.
Not metaphorically. Literally.
Two feet of heavy snow, capped with a stubborn crust of ice from the most recent storm, sealed in the driveway, the walkway, the mailbox, and most frustratingly, the car. The top layer was solid enough to stand on in some places. Underneath it sat dense, compacted snow that felt more like wet cement than powder. The newscasters called it “snowcrete.”
To get to the car, I had to chip away at the icy surface first. Metal scraping against frozen glaze. Then dig through the snow beneath. Layer by layer. Mailbox next. Pathway after that. It was slow work. Physical. Cold.
But then something shifted.
The temperature ticked upward.
The afternoon sun leaned in.
Drips started forming at the edge of the roofline.
And the question hit me: How long will all of this take to melt?
That question is not just practical. It is mathematical. And it is a powerful way to teach children how the world works.
Turning Snow into a Math Problem
When children look at a snowbank, they see something to climb. Or avoid. Or tunnel through.
When we look at it through a STEM lens, we see variables.
• Depth of snow
• Density of snow
• Air temperature
• Ground temperature
• Solar radiation
• Wind speed
• Surface area exposed
Melting is not magic. It is energy transfer.
At its simplest, snow melts when temperatures rise above 32 degrees Fahrenheit. But that is only the surface story. A cloudy 35 degree day behaves very differently than a sunny 35 degree day. Wind accelerates evaporation. Dark pavement absorbs heat faster than white lawns. Compacted snow melts more slowly than fluffy powder.
Students can measure snow depth in inches. They can record daily high temperatures. They can track hours of sunlight. They can chart melt rates in inches per day.
Now suddenly we are teaching:
• Data collection
• Unit conversion
• Rate of change
• Graphing
• Prediction modeling
• Real world application of algebra
And they are motivated because the problem is right outside their door.
Calculating Melt Time
Let us simplify. Imagine:
• 24 inches of snow
• Average daytime temperature of 38 degrees
• Overnight low of 30 degrees
• Moderate sun exposure
If the snow is melting at roughly 1 to 2 inches per day under those conditions, students can:
• Estimate total melt time
• Create best case and worst case scenarios
• Graph projected decline over time
• Adjust predictions as new data comes in
They quickly discover that melting is not linear. It accelerates when more dark ground is exposed. It slows when overnight refreezing occurs. It changes when rain enters the equation.
And that is where the real learning happens.
Introducing AI into the Inquiry
Now layer in AI. AI can help students:
• Analyze historical temperature patterns
• Compare melt rates from past storms
• Model different weather scenarios
• Simulate “what if” temperature shifts
• Visualize rate of change curves
Instead of replacing thinking, AI amplifies it.
Students can ask:
What if temperatures stay above freezing for 48 hours straight?
What if we cover part of the snowbank with a dark tarp?
What if wind speeds increase?
AI can help generate simulations quickly. But students still need to ask the right questions. They still need to interpret the results. They still need to validate whether the outputs make sense.
The goal is not faster answers. It is deeper questions.
From Driveway to Design Thinking
What started as frustration in my driveway became a design challenge in a classroom.
Can students:
• Design the fastest way to clear a path using energy transfer principles?
• Compare manual removal versus passive melting?
• Calculate the cost of salt versus time saved?
• Predict when bus routes become safe?
Suddenly snow melting is not just weather. It is physics, economics, environmental science, and civic planning.
Mission-Aligned AI in Action
For school leaders, this is what responsible AI integration looks like.
It does not begin with a tool. It begins with a question grounded in lived experience. AI is not there to tell students how long snow will melt.
It is there to help them model, analyze, and challenge their own predictions. The snowstorm becomes:
• A math lab
• A science experiment
• A data science project
• A real world systems study
And perhaps most importantly, it becomes joyful.
Because nothing motivates a learner like wanting to know when they can finally see the grass again.
Asking the Right Questions
As we move through winter and into spring, I encourage school leaders and educators to look outside before looking at a dashboard.
What real world phenomena are sitting in your parking lot right now?
How might AI help students explore it more deeply rather than bypass it?
At Arcadia, we help schools design mission-aligned AI integration that starts with inquiry, not efficiency. If you are ready to turn everyday experiences into authentic, data-rich learning opportunities, let’s build that framework together.
Sometimes the best curriculum is buried under two feet of snow.
And sometimes, all it takes is a little warmth and the right question to make it melt.
James “Jimi” Purse, Founder
Arcadia Education Partners
Partnering with schools to align leadership, communication, and innovation. inspiredbyarcadia.com
