The question I ask every winter

Every year I try to lock in lodging early for Whistler to catch lower prices — but the question that always drives the decision is the same: will there be enough snow for my trip? Historical snow data is the most reliable signal I have. So I went to SNO's Whistler snow history page, and while the underlying data is genuinely useful, the visualization makes the story harder to read than it needs to be.

This post is a focused critique of one dimension: color choice. It's a seemingly small design decision, but it has an outsized effect on clarity and trust — especially for a visualization that people rely on to make real travel decisions.

The palette problem

The SNO charts attempt to show multiple measures — snowfall, base depth, historical averages — often within the same plot. The visuals lean heavily on several shades of the same hue (blue), which creates three compounding problems:

1. Low distinction: similar blues for different measures force viewers to rely on legends or hover states rather than perceiving the data at a glance. The chart's job is to make comparisons effortless; a monochromatic palette does the opposite.
2. Poor contrast: thin lines and light-colored bars against a pale background reduce legibility, particularly on mobile screens or in bright daylight — which is, ironically, when most skiers are checking snow reports.
3. Accessibility gaps: approximately 8% of men have some form of color-vision deficiency. Distinguishing similar blues is one of the hardest tasks for those viewers. Relying on hue alone to encode different metrics excludes a meaningful share of the audience.

A cleaner palette approach

Here is a straightforward palette that improves clarity while keeping a cool, wintry aesthetic:

Three targeted edits with the highest impact

If I were updating the SNO Whistler page, I would start with these three changes — they require minimal effort but materially improve comprehension:

1. Assign distinct hues to core metrics. Deep blue for base depth, white/light-gray for new snowfall, and a warm accent for historical averages and anomalies. One metric, one color, applied consistently across every chart on the page.
2. Increase stroke width and marker size. Thin lines in line charts become nearly invisible when two series overlap. A minimum 2px stroke width and slightly larger data-point markers maintain legibility at small viewport sizes.
3. Add short textual summaries. A single sentence beneath each chart — "By December 4, median base depth is X cm; recent decades trend Y" — dramatically reduces the cognitive load for users who just want a quick answer.

Why this matters beyond aesthetics

People do not consult snow-history pages for aesthetics — they consult them to make travel decisions that involve real money: flights, lodging, lift passes. A visualization that hides distinctions with near-identical hues is not neutral. It increases uncertainty, forces cross-referencing with other sources, and erodes confidence in the data itself.

Strong, accessible color choices are low-effort, high-impact improvements. They cost nothing to implement in a chart library and immediately increase how much a viewer can trust what they're reading. For a tool that skiers and snowboarders rely on every season, that trust is worth designing for.