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tblevins

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About tblevins

  • Birthday 09/19/1963

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    tblevins1
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    http://www.employees.org/~tblevins/todd.html
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    tblevins1

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  • Four Letter Airport Code For Weather Obs (Such as KDCA)
    krah
  • Gender
    Male
  • Location:
    Raleigh, NC
  1. Snow had all but stopped about 25 minutes ago around davis and kitt creek road...about 10 minutes ago, it started back about as hard as anytime today.
  2. Interesting to see the headlines today blaming the models for the failure to forecast the storm for areas around NY/NJ.
  3. In light of the current discussion surrounding the ongoing blizzard/not blizzard in the northeast and the model hugging by some to predict the outcome, I was thinking tonight, what characteristics make up a good weather forecaster. I'm certainly not versed in the sciences like many on this forum, so this is more of an amateur look at the subject. Here's my list: - understand your own limitations: know your blind spots and lack of knowledge, - know the model biases for your area: how many models are available now? some can do a great job with certain environments and areas, some can be downright awful. There are some on these forums that have said to get rid of model <x>. But in some cases, that model has been spot on with regards to the outcome - Know your micro-climate: what can go wrong with a particular forecast? proximity to a particular region (ocean, mountains, etc) - know your audience: if you announce a 10% chance of snow 10 days out in NYC, then no one will notice..if its somewhere south of I-40, its a promise or guarantee - understand the statistics: if a model is showing a 50 year storm, then be very cautious...even if all the models have latched on to the solution, does it mean its really going to happen? The likelihood is very slim based on statistics...it might actually turn out to be a 5-10 year storm. Also, its interesting to "handicap" those who attempt to forecast the next event on these forums. Some hang on to every positive outcome that may exist for winter weather and some grab on to the most negative of predictions, even if its an outlier model.