Snow accumulation fields are a trap. Not because the data is wrong, but because the fields measure different things — and almost no one reads the docs carefully enough to catch where the boundaries fall until their app starts reporting 0 cm of snow at 12:01am during an active blizzard.
Here’s what’s actually happening, and how to build around it.
What the Fields Are Actually Measuring
WeatherAPI returns snow data in a few different places depending on which endpoint you’re hitting. The forecast.forecastday[].day.totalsnow_cm field is a daily aggregate — it resets at local midnight. forecast.forecastday[].hour[].snow_cm is the modeled snowfall for that specific hour. They’re not the same thing reported at different granularities. They’re computed differently and can diverge for the same reason daily and hourly precipitation totals sometimes don’t reconcile cleanly.
The hourly snow_cm value comes from NWP model output — HRRR for the US at 3km resolution, NAM at roughly 12km, GFS for longer-range. The daily total aggregates across the forecast day, but “the forecast day” is bounded by local midnight, not by any meteorologically meaningful event. A storm that starts at 10pm and drops 15cm by 6am the next morning shows up as two separate daily events, with the bulk of accumulation landing in the second day’s total. If your app reads totalsnow_cm from the current day only, it will show almost nothing through most of the active storm.
This is the most common complaint I see for snow-adjacent use cases: “the API said 1cm but we got 20cm overnight.” In nearly every case, the developer was reading totalsnow_cm for the day the storm started, not integrating across the day boundary where the accumulation actually landed.
The Midnight Reset Is Not a Bug
Daily boundaries have to be drawn somewhere, and local midnight is the standard. The problem is that storm events don’t care about midnight. A classic winter clipper in the upper Midwest often arrives late evening and peaks in the early morning hours — exactly the split that makes naive daily-total reads useless for storm tracking.
The model producing the hourly snow_cm values doesn’t reset at midnight. Each hourly value represents that hour’s contribution independently. So the fix is to stop relying on the daily aggregate for anything storm-tracking-related and build your own accumulation window from the hourly fields.
Building a Storm-Total Accumulation Window in Practice
The approach is straightforward: request multiple forecast days, iterate the hourly array, and sum snow_cm across whatever window actually represents your storm. The tricky part is defining that window automatically rather than hardcoding a time range.
A practical heuristic: find the first hour where snow_cm > 0 and trace forward until you hit N consecutive hours of zero snowfall. That gap-based termination handles both brief lulls within a storm and the actual end of the event, without requiring you to know in advance when the storm ends.
// Pseudocode — adapt to your language/SDK
const hours = getAllForecastHours(response); // flatten forecastday[].hour[] across all days
const GAP_THRESHOLD = 3; // consecutive zero-snow hours = storm ended
let inStorm = false;
let gapCount = 0;
let stormTotal = 0;
let stormStart = null;
for (const hour of hours) {
const snow = hour.snow_cm ?? 0;
if (snow > 0) {
if (!inStorm) {
inStorm = true;
stormStart = hour.time;
}
gapCount = 0;
stormTotal += snow;
} else if (inStorm) {
gapCount++;
if (gapCount >= GAP_THRESHOLD) {
break; // storm over
}
}
}
console.log(`Storm starting ${stormStart}: ${stormTotal.toFixed(1)} cm total`);
One gotcha: flatten forecastday[].hour[] across all returned days before iterating. If you loop day-by-day and reset state at each day boundary, you’ll re-introduce the midnight-reset problem in your own code. Keep the hourly array as one continuous timeline.
How Many Days of Forecast Do You Actually Need?
For active storm tracking — the kind where someone wants to know how much will fall before it stops — two days is usually enough. Most synoptic-scale winter storms clear within 24-36 hours of onset. For planning purposes you want 3-5 days, but model skill for snow amounts degrades faster than it does for temperature or precipitation type. HRRR is genuinely useful out to about 18 hours for snow accumulation. Beyond 48 hours, GFS-driven snowfall numbers are rough order-of-magnitude guidance, not precise totals — the NWS says as much in how they communicate extended snow forecasts.
I wouldn’t build a UI that shows a specific figure like “expect 23 cm” for anything more than two days out. A range is more honest. The model doesn’t really know either, at that point.
Historical Snow: The Separate Problem
If you need to show how much has already fallen — observed accumulation, not forecast — the history endpoint is what you want, and the same logic applies. Sum hourly snow_cm from the history response. The daily total in historical data is still just a midnight-bounded aggregate.
One real limitation: observed snow data quality degrades significantly outside dense station networks. METAR stations report present weather and visibility, not snow depth directly. The snow accumulation figures in history responses are model-derived analysis fields, not direct gauge measurements. In areas with sparse coverage — rural Canada, much of Scandinavia, mountain terrain — treat historical snow_cm as a model estimate. That distinction matters if you’re doing anything like claims processing or damage assessment where observed truth is legally relevant. For most application use cases it’s fine, but don’t represent it as station-observed data to your users.
A Note on the snowdepth Field
Some responses include a snowdepth or equivalent field. That’s accumulated depth on the ground, not incremental snowfall — it’s affected by compaction, melt, and sublimation, and it will not equal the sum of hourly snow_cm values over any window. If you need depth (for load modeling on structures, or avalanche risk estimation), that’s your field. If you need how much fell during a storm, you want hourly snowfall summed. They’re different physical quantities, and conflating them is a recurring error in agriculture and transportation use cases.
Before you ship: what does your app actually need to know — how much snow fell during the event, or how much is sitting on the ground right now? Most of the bugs described above trace back to not clearly answering that question at the start.