At SnowSense™, we are committed to providing our users with highly accurate, unbiased, and data-driven meteorological content. These guidelines establish the core principles that our authors and editorial team follow.

1. Accuracy & Data Sources

All weather predictions, blog posts, and educational guides must be based on verifiable meteorological data. We rely exclusively on publicly available, peer-reviewed, or government-issued data sources, primarily including:

The National Weather Service (NWS)
National Oceanic and Atmospheric Administration (NOAA)
Open-Meteo and associated high-resolution forecast models (HRRR, ECMWF, GFS)

Authors are strictly prohibited from sensationalizing weather events or predicting extreme outcomes without clear, modeled backing from these reputable sources. When multiple models disagree, we disclose the range of outcomes rather than presenting a single prediction as certain.

Our snow day probability engine synthesizes data from these sources in real-time, weighing each model's historical accuracy for the user's region. We do not cherry-pick the model that produces the most dramatic result.

2. Objectivity & Bias

Our content is purely informational and educational. We do not endorse specific commercial products without clear disclosure, and we maintain strict independence from advertising influence. All editorial decisions are made independently of any sponsored partnerships.

When discussing school closure decisions, we present the facts of each case without implying negligence or incompetence on the part of school administrators. Different regions have different infrastructure and risk tolerances, and our content reflects that nuance.

3. Correction Policy

Despite our reliance on automated models and expert review, errors can occur. If a factual error is discovered in our editorial content or blog guides, we will promptly update the article and, if necessary, provide a correction note at the top or bottom of the page detailing what was changed and when.

For our real-time prediction engine, accuracy is continuously monitored against actual closure outcomes. When our model consistently over- or under-predicts for a specific region, we adjust the closure threshold calibration and document the change in our methodology notes.

4. Originality & Plagiarism

All content published on SnowSense™ must be original. We strictly prohibit plagiarism. While we aggregate data from external APIs, any analysis, synthesis, or educational writing must be generated by our editorial team.

When we reference external research, government reports, or meteorological studies, we cite the source clearly and link to the original material where possible. Our goal is to be a primary source of snow day analysis, not a re-publisher of existing content.

5. Review Process

Before any static educational or editorial piece is published on our blog or guides section, it undergoes review by at least one other team member. The review ensures that the content aligns with our persona ("The Empathetic Nerd") — meaning it is precise, accessible, logically sound, and genuinely helpful.

Technical claims about weather modeling, closure thresholds, or atmospheric science are verified against the cited data sources. If a claim cannot be substantiated, it is removed or rephrased to accurately reflect the uncertainty.

6. Regional Sensitivity

Snow day thresholds vary dramatically across the United States. Six inches of snow may be routine in Minnesota but catastrophic in Georgia. Our content and predictions always account for regional context — we never apply a single national standard to school closure probability.

When writing about specific cities, districts, or states, our authors research the local infrastructure, historical closure patterns, and climate zone to provide regionally accurate information rather than generic advice.

Last updated: July 3, 2026

Editorial Guidelines