Why Snowstorm Forecasts Vary So Widely (Full Transcript)

Snow totals swing because models are imperfect, snow ratios amplify small errors, and near-freezing temperatures can flip precipitation between snow and rain.
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[00:00:00] Speaker 1: Why do snowstorms have wildly fluctuating forecasts? I mean, I've once seen a mayor say there'd be between three to 16 inches of snow. But why? To help forecast the precipitation and temperature, we use models. None of them are perfect. It's impossible to perfectly map out mathematically or measure the atmosphere. There are well over 50 models with a lot of assumptions. Two that you'll often hear about are the European model or the GFS, an American model. The European model costs hundreds of thousands of dollars for full access while the GFS is totally free. Let's start basic though. A rough estimate is that for every 10 inches of snow, there's an inch of liquid. Few would care if the forecast called for 0.3 inches of rain and 1.6 inches fell. But in snow terms, that would be the difference between three inches of snow and 16 inches of snow. Beyond that, nobody cares if a forecast is off by a degree if it's 77 degrees versus 78. But 32 degrees versus 33 degrees could mean the difference between snow and rain. So which is it? Which model reigns supreme? Well, statistically speaking, the Euro model has been proven to be more accurate over the past two decades. Of course, it's not perfect all the time, like the time it famously botched the 2015 blizzard here in New York. But that is why forecasting is no easy science.

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Arow Summary
Snowstorm forecasts vary widely because weather models are imperfect and small errors in predicted liquid precipitation and temperature can translate into large differences in snowfall amount and type. Many models exist with differing assumptions; commonly cited ones are the European (ECMWF) and the U.S. GFS. A typical snow-to-liquid ratio is about 10:1, so a modest difference in liquid equivalent can mean several inches versus over a foot of snow. Likewise, a 1°F error near freezing (32–33°F) can shift precipitation from snow to rain. Statistically, the European model has been more accurate over recent decades, though it can still fail in notable events like the 2015 NYC blizzard, underscoring the difficulty of forecasting.
Arow Title
Why Snowstorm Forecasts Swing So Much
Arow Keywords
snowstorm forecast Remove
weather models Remove
ECMWF Remove
GFS Remove
snow-to-liquid ratio Remove
temperature threshold Remove
forecast uncertainty Remove
precipitation type Remove
model assumptions Remove
blizzard 2015 Remove
Arow Key Takeaways
  • Forecasts fluctuate because atmospheric modeling and measurements can’t be perfect, and models rely on assumptions.
  • Snowfall totals amplify small liquid-precipitation errors due to typical ~10:1 snow-to-liquid ratios.
  • Near-freezing temperatures make precipitation type highly sensitive; 32°F vs 33°F can mean snow vs rain.
  • Dozens of models exist; ECMWF (European) and GFS are two widely referenced examples.
  • ECMWF has been statistically more accurate on average, but no model is flawless, as seen in notable misses like the 2015 NYC blizzard.
Arow Sentiments
Neutral: Explanatory, informational tone focused on reasons for forecast uncertainty, model differences, and measurement limits without strong emotional language.
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