[voyager2]

  I use the forecast put out by weather.gov that's supposedly tailored

to the square mile because it gets the 12 hour forecast right about 40% of the time. The others are worse.

  Of course, when they say "chance of precipitation is 80%, less than an
  inch possible" and it doesn't rain, the forecast is semantically
  "correct."

  Like the El Nino impact on the SE US, where they forecast a 50% chance

of drier colder weather and a 50% chance of warmer wetter weather, it's nearly impossible to be wrong.

  Maybe this is the sort of obfuscatory probabilistic forecast Mr. Meyer

is counting as "accurate."

[maaaats]

> it gets the 12 hour forecast right about 40% of the time

Where are these figures from? Your emotions, or something scientifically rigid? If the former, having this discussion is meaningless.

[voyager2]

"chance of precipitation is 80%, less than an inch possible"

How would you determine, in a scientifically rigid manner, the limits on conditions which would validate that forecast as "right?" Or the inverse. What conditions would invalidate it as "not right?"

[scott_s]

In a top-level comment upthread, jacques_chester posted a link to an overview of forecast verification methods: http://www.cawcr.gov.au/projects/verification/

[Majromax]

> I use the forecast put out by weather.gov that's supposedly tailored to the square mile because it gets the 12 hour forecast right about 40% of the time.

Unfortunately, this is an example of false precision. The highest resolution numerical forecasts run by NOAA have a grid of about 3km, already coarser than your one-square-mile "tailored" output. The effective resolution of numerical weather models is also 2-3 times coarser than their grid spacing (because of numerical diffusivity and similar effects).

What you're seeing isn't a "tailored" output, but instead an interpolated result from a coarser grid.

Forecasting of very high-resolution effects is the subject of active and ongoing research, but unfortunately popular meteorology does not do a good job of discussing current limitations.

Look at the confusion in this set of comments, for example, about what degree of forecast error is normal/acceptable.

[voyager2]

If I move my forecast location a few hundred yards, my elevation changes rather drastically, changing the forecast. Elevation isn't a factor most forecasts even consider. Is it false precision to use elevation to tailor the forecast?

[Majromax]

In that case, the interpolation includes a vertical component as well. You'll see the effects of the lapse rate (change in temperature with height).

That's great for you, but it means that your forecast (of [my elevation, my coordinates]) is not much more accurate than one for ([my elevation, my coordinates plus a few hundred yards]).

More technically: "surface" isn't a smooth variable when elevation changes quickly, but interpolation like that performed by weather.gov necessarily works on smooth fields. Applying post-facto elevation is great and worthwhile, but it doesn't improve the accuracy in a technical sense.

[kryogen1c]

No doubt.

That's like saying horoscopes and fortune-tellers are stunningly accurate: "a positive opportunity will present itself to you today - you only have to open the door" "you are holding a secret pain that is preventing you from moving on. Find peace and let go"