[mathsmath]

I find their error model to be off for my location. I'm in a mountain valley in Colorado, and forecasting the weather is like throwing darts at the wall (especially with precipitation).

We have no radar coverage, and the only "official" sensor I know of is at our local airport. It was recently upgraded to be somewhat accurate, but isn't operational about half the time. The most accurate forecast we have is the text synopsis from a forecaster in Pueblo talking about how it "might be stormy in the afternoons this week."

Our nearest city is a 2.5 hr drive, so we're in a pretty big gap of weather coverage!

Anyway - hope someone finds this interesting. I envy those of you with accurate weather! It has been interesting moving out here.

[mckirk]

Something I learned only recently: Weather forecasts don't actually directly use sensor data. Instead, a physically consistent model is first fitted to all the available sensor data, and then the forecast is made based on the values that model produces. Doing it this way has the benefit that physically implausible sensor readings are given less importance, and the fact that this model can be sampled in regular intervals, whereas the sensors are all over the place (and often moving, e.g. in aircraft, which contribute crucial data).

Of course, higher density of sensors would lead to a better fit of the model to the real world, but there would still be no guarantee that the model would reflect the measured values exactly. I found that pretty interesting.

(And it's kind of funny to think about our own consciousness in this way, which seems to work somewhat similarly: we don't experience the actual 'sensor values', but instead we experience the output of a model our brain fits to those inputs.)