Snow-to-liquid ratio estimator

The result tells you how many equal depth units of snow may accumulate from one depth unit of liquid precipitation: a 10:1 ratio means roughly ten inches of snow from one inch of liquid, or ten centimetres from one centimetre. It is a planning heuristic rather than a site-specific forecast, but it provides a transparent starting point for interpreting a quantitative precipitation forecast.

What a snow-to-liquid ratio means

Weather forecasts often provide liquid-equivalent precipitation because rain and melted snow can be compared on one consistent scale. Snow depth, however, depends on how much air is trapped between crystals. The snow-to-liquid ratio, sometimes called the fluff factor, expresses that expansion. A ratio of 10:1 indicates that one inch of melted water corresponds to about ten inches of newly fallen snow; the same dimensionless relationship works in centimetres or millimetres. Enter the representative air temperature while snow is falling, and choose Celsius, Fahrenheit, or kelvin. The estimator converts that reading to Celsius internally, follows a fixed temperature curve, and reports a central ratio plus a twenty-percent practical range. The output also labels the snow as dense, average, fluffy, or very fluffy. Multiply expected liquid precipitation by the ratio to obtain a first-pass snow accumulation. For example, 0.5 inch of liquid at a 12:1 estimate suggests about six inches of fresh snow before settling. The range is useful because a single exact number can imply more confidence than temperature alone supports.

How the temperature estimate is calculated

The calculator uses a bounded piecewise-linear heuristic rather than pretending that one universal equation describes every storm. Fixed reference points associate temperatures from very cold conditions through near-freezing conditions with typical ratios. Between adjacent points, the engine interpolates in a straight line, producing smooth changes instead of abrupt jumps at arbitrary thresholds. Ratios generally decrease as the temperature approaches freezing because wetter crystals pack more densely. The curve peaks below very cold but not extreme conditions, reflecting the common planning expectation that cold dendritic snow can be especially fluffy, while the coldest air is not assigned an unlimited ratio. Inputs colder than minus one hundred or warmer than twenty degrees Celsius are rejected because they fall outside a credible snowfall-planning domain. The returned practical range is eighty to one hundred twenty percent of the central estimate, with a conservative lower floor. Every step is deterministic: identical inputs always return identical JSON, and the algorithm makes no network request, reads no live forecast, uses no random value, and consults no clock. That makes it suitable for reproducible worksheets, forecast-processing scripts, and integration tests.

Use the result as a planning estimate

Temperature is only one control on snow density, so treat this result as a baseline rather than an official accumulation forecast. Crystal habit depends on moisture and temperatures through a deep layer of the atmosphere, not merely the surface thermometer. Strong wind can break crystals and pack snow tightly; riming can add dense supercooled droplets; mixed precipitation can sharply lower the apparent ratio; and ground warmth, traffic, solar energy, drifting, and settling can reduce measured depth after flakes land. A forecast sounding, local climatology, radar trends, and observations from the storm should take priority when they are available. For quick planning, choose a temperature representative of the main accumulation period rather than the daytime high or an isolated overnight low. Multiply the forecast liquid amount by the central ratio, then repeat with the low and high values in typical_range to form a reasonable scenario band. The API costs $0.002 per successful item and returns the normalized Celsius temperature, ratio text, equivalent snow depth per unit of liquid, classification, method name, and an explicit caveat. Missing or invalid temperatures fail validation instead of returning a misleading default.