Perplexing Probabilities

There are a host of challenges posed in the forecast communications business.  One that I thought of this morning as I surveyed the ensemble forecasts is the low probability, high impact weather event.

Fair weather looks to predominate over northern through Thursday, but on Friday, an upper-level trough moves across the northwest U.S. with the trailing cold front racing across Utah.  The NAM calls for precipitation accompanying the front to be relatively light.  Perhaps some valley rain showers and mountain snow showers, but nothing for skiers to get excited about.  

If we look at our downscaled forecasts for Alta based on the Short Range Ensemble Forecast System (SREF) we find that most members are producing very light accumulations of 0.25" of water equivalent or less through 6 PM Friday (0000 UTC 21 October).  Again, nothing to get excited about.  However, 2 of the 26 members are going bigger and putting out about 0.7" of water or so.  

If we look at our downscaled NAEFS forecasts for Alta, most members producing light accumulations, a few in the 0.4" to 0.7" range, but then two outliers that go absolutely huge, generating about 2.5 inches of water and around 25 inches of snow.  Skiing anyone?

Such outliers are unusual, but not unheard.  However, I don't know of any studies that have attempted to look specifically at the reliability of such low probability, high impact forecasts.  The NAEFS forecast above, if taken literally, would yield about a 10% chance of 20" of snow or more on Friday, but a 90% chance of 7 inches or less.  Is that a reasonable forecast of the probabilities?  In addition, if that was a reasonable forecast of the possible outcomes, how best to communicate that to the public and forecast customers?  "Well, we think that there will be some snow showers.  Odds are it won't add up to much, but there's a slight chance of 20."  That should go over well.

I don't have answers for these questions.  We need better validation studies of our ensembles and, as ensembles improve, better ways to both extract and communicate probabilistic forecast information in a way that is useful to the end user.