Forecasting Methodology

Last updated: December 8, 2025

How We Turn Tomorrow’s Weather Into a Snow-Day Probability

Winter weather in Canada can change quickly, and school closures depend on more than just snowfall amounts. This page explains, in clear, simple terms, how the Snow Day Predictor estimates the likelihood of a snow day in your region. Our goal is to be fully transparent about the data we use, the signals we monitor, and the limitations of an unofficial, forecast-based tool.

Understanding the Purpose of the Predictor

School boards, bus companies, and local authorities use a wide range of factors to decide whether buses run or schools close. Snow Day Predictor isn’t meant to replace those decisions; instead, it helps families:

• Plan ahead on evenings when storms are approaching
• Understand which weather patterns often lead to closures
• Get a quick sense of whether tomorrow might bring a snow day

This methodology explains how we translate forecast data into a simple, helpful percentage.

Step One: Matching Your Postal Code to a Region

When you enter a Canadian postal code or FSA, the tool determines the approximate region associated with that code.

We do this using WeatherAPI’s Search API, which helps us identify:

• A nearby city or community name
• The corresponding province
• Geographic coordinates for pulling weather data

Suppose a postal code is too broad or doesn’t match a precise community. In that case, the tool defaults to a province-level regional lookup, ensuring we can still generate a forecast-based estimate.

This lookup is anonymous; no personal details or identifiers are collected.

Step Two: Gathering Tomorrow’s Weather Data

Once we know the region, we retrieve tomorrow’s weather forecast using Open-Meteo, a trusted, publicly available weather service.

The forecast includes:

• Snowfall totals (snowfall_sum)
• Precipitation amount (precipitation_sum)
• Weather code, which identifies types of snow or mixed precipitation
• Wind speed, including gusts
• Temperature highs and lows

Because conditions change quickly, the predictor always uses the most up-to-date forecast available at the time you check.

Step Three: Identifying Key Snow-Day Indicators

Over time, certain patterns repeatedly appear on days when buses are cancelled or schools are closed.

Our model watches for these patterns by evaluating several key signals:

a. Snowfall Amount

Significant overnight or early-morning snowfall is one of the strongest predictors of cancellations.

b. Temperature Extremes

Very cold temperatures or severe wind chill can impact student safety and transportation.

c. Wind and Visibility

High winds can create blowing snow, whiteouts, and drifting conditions that make rural routes unsafe.

d. Precipitation Type

Weather codes help identify whether the storm involves:

• Heavy snow
• Ice pellets
• Freezing rain
• Mixed winter conditions

Icy conditions are a major factor in many regions.

Step Four: Weighted Scoring

Each weather signal contributes differently to the final prediction.

For example:

• Heavy snowfall heavily increases the probability
• Strong winds or blizzard conditions add additional weighting
• Temperatures above freezing may reduce the likelihood
• Forecasts indicating mixed precipitation or ice increase the risk in certain regions

The model uses a rules-based scoring system, not machine learning or a proprietary black box, so its behaviour is predictable, consistent, and easy to interpret.

Step Five: Producing the Final Percentage

After evaluating the relevant weather indicators, the tool produces a percentage from 0% to 100%, representing how closely tomorrow’s conditions resemble typical snow-day scenarios in Canadian regions.

• 0–20% → Unlikely
• 20–50% → Some possibility
• 50–80% → Conditions match common closure patterns
• 80–100% → Strong alignment with known snow-day scenarios

These aren’t guarantees; they’re weather-informed signals to help you prepare for the next day.

Why Percentages May Vary Between Regions

Canada’s geography and climate are incredibly diverse. A storm that causes closures in one area may not affect schools the same way elsewhere.

Examples:

• Ontario & Quebec: Heavy overnight snow + blowing snow often triggers bus cancellations.
• Prairie Provinces: Extreme cold and wind chill are major factors, sometimes more than snowfall.
• Atlantic Canada: Freezing rain and mixed precipitation can lead to widespread closures.
• British Columbia: Steep terrain and rapid snowfall in coastal areas can create sudden disruptions.