Where the Data Comes From
All live weather data used for predictions is sourced from the Open-Meteo API — a free, open-source weather API that aggregates forecast output from leading numerical weather prediction (NWP) models. Open-Meteo does not require an API key, making the data freely accessible without commercial licensing costs that would restrict the service.
Open-Meteo ingests forecast data from multiple NWP models, including:
- GFS (Global Forecast System) — operated by NOAA, updated every 6 hours
- ECMWF IFS (European Centre for Medium-Range Weather Forecasts) — one of the highest-accuracy global models
- GEM (Global Environmental Multiscale) — operated by Environment and Climate Change Canada
- Regional models for higher-resolution local forecasts where available
The specific model blend used for a given location depends on Open-Meteo's internal routing, which selects the best available model for each geographic area.
Which Variables Are Used
Snow Day Calculation requests the following forecast variables for the prediction window (typically the next 24 hours, with focus on overnight and early morning):
| Variable | Why It Matters | Source |
|---|---|---|
| Snowfall accumulation (cm/in) | Primary driver — total expected snow is the most direct indicator of closure risk | Open-Meteo forecast |
| Temperature (°C / °F) | Sustained sub-freezing temps increase ice risk and closure likelihood even without heavy snow | Open-Meteo forecast |
| Apparent temperature / Wind chill | Extreme wind chills can trigger closures independently of snowfall | Open-Meteo forecast |
| Precipitation probability (%) | Indicates confidence that precipitation will actually fall | Open-Meteo forecast |
| Precipitation type | Distinguishes snow, freezing rain, sleet — each with different road safety implications | Open-Meteo forecast |
| Wind speed (km/h / mph) | High winds reduce visibility and compound temperature effects | Open-Meteo forecast |
How Prediction Works
The prediction model is a rule-based, multi-factor scoring system. It is not a learned machine learning model trained on historical closure data (because such labeled training data is not publicly available at scale). Instead, it uses domain-informed rules that approximate the decision logic real school administrators use.
Here is how the score is computed at a high level:
- Fetch forecast — live weather variables are retrieved for the queried location
- Evaluate individual factors — each weather variable is scored on a 0–1 scale based on thresholds
- Apply regional weighting — the regional factor adjusts sensitivity based on latitude. Southern cities (e.g., Atlanta) have lower historical closure thresholds than northern cities (e.g., Minneapolis), so the same 3 inches of snow produces a higher score in the south
- Aggregate score — weighted factors are combined into a final 0–100% probability
- Generate explanation — the dominant factors are surfaced as a plain-language summary
Historical Data
For the "Historical Snowfall Trends" chart, Snow Day Calculation queries the Open-Meteo Archive API (archive-api.open-meteo.com), which provides historical weather observations going back several decades. This data is loaded on demand (when you click "Load Historical Data") to avoid unnecessary API calls. Historical data is used for context only — it does not directly influence the current-day prediction score.
Update Frequency
Forecast data from Open-Meteo is updated multiple times per day as new model runs complete. Weather queries made through Snow Day Calculation reflect the most recent available forecast at the time of the request, subject to a short client-side cache (approximately 10 minutes) to prevent redundant API calls.
If you check the prediction at noon and again at 6 PM, you may see different results — this is expected. Forecasts sharpen as a storm approaches. We recommend checking in the late afternoon for the most accurate next-morning prediction.
Geocoding
When you type a city name or postal code, it is converted to geographic coordinates using the Open-Meteo Geocoding API and/or the BigDataCloud reverse geocoding API (for GPS-based lookups). The resulting latitude/longitude is then used to fetch the weather forecast. Geocoding results are cached locally in your browser for approximately 60 minutes.
Limitations
We want to be honest about what the model cannot do:
- We do not have access to school district closure thresholds. Individual districts have their own internal policies — some close for 2 inches, others for 6 — and these are not publicly available in machine-readable form. Our regional factor is a generalization, not district-specific data.
- We cannot account for infrastructure factors. Bus availability, road treatment, staff living distances, and pre-existing ice from prior days all influence real closures and are not captured in weather forecasts.
- Forecast uncertainty increases with lead time. A prediction made 48 hours in advance is less reliable than one made 12 hours in advance. We display a confidence indicator to reflect this.
- We rely on third-party data. If the Open-Meteo API is unavailable or inaccurate, our predictions will be affected. We do not independently verify forecast data.
- Rural vs. urban differences are not fully modeled. Rural areas often close more readily due to longer bus routes and less infrastructure maintenance. This is not separately accounted for in the current model.
Transparency Statement
Snow Day Calculation is an independent, unofficial tool. We make no claim to be a professional meteorological service. Every prediction is clearly labeled as an estimate. We will not inflate accuracy figures, fabricate historical performance claims, or misrepresent how our model works. If something changes — data sources, model logic, update frequency — this page will be updated to reflect it.