Weather Models and the #Snowquester Snowstorm

There is finally snow on the horizon.

Meteorological spring kicked off on March 1st (this should not be confused with astronomical spring during your regularly-scheduled equinox), and with it came the faint promise of snow in the weather models. The further out in time we are attempting to predict something, the more noise we are going to have in our data. Part of a meteorologist’s job is finding the signal within the noise in the information we have available (weather balloon data, numerical model output, ensemble forecasts, and other things).

You may be familiar with the “butterfly effect,” which suggests that the formation of a hurricane may be dependent on a butterfly flapping its wings weeks earlier. While an over-exaggeration, Edward Lorenz, a mathematician and meteorologist, used this simple example to demonstrate the universe’s sensitivity to initial conditions. In other words, a seemingly small (perhaps overlooked) detail may have a profound effect on the end result of a mathematical/scientific/meteorological prediction model.

If our starting conditions are wrong, even at a very small level, the predictions our weather models make get increasingly worse with time. But it gets even better! If the equations you use in your models are approximated at all, those small differences add up over time as well. Let’s use a familiar example:

2 + 2 = 4

Now what if that was a simplification of a slightly more complicated problem?

2.4 + 2.4 = 4.8

With these values, most third graders could tell you that it’s typical practice to round the value of 2.4 down to 2. But there is a big difference between 4 and 4.8, right? What if we continued adding these approximations together over time?

2 x 1000 = 2000

2.4 x 1000 = 2400

Now we’re up to a 400 [unit] difference!

That’s how weather models work, in a nutshell. We can’t start with perfect data and we can’t use perfect numerical values within perfect mathematical equations. The very minute changes we have to make to account for computing power limitations mean that our predictions further out are less certain.

Now that I’ve tricked you all into a science lesson, back to #Snowquester. This storm is looking to be the best chance for snow that the D.C. area has had in awhile. There is still some uncertainty in the models (as was just explained), but the closer we get to the event, the more the different models come into agreement. There is a chance for over a foot of snow in the higher elevations west of D.C., and the Capital Weather Gang currently has the 12”+ bullseye around Winchester, Virginia.

The temperatures west of the District should remain low enough to keep the precipitation falling as snow throughout almost the entire event, while parts of D.C. and east will begin with rain before the changeover to snow overnight Tuesday into Wednesday morning. Because the temperatures will be hovering around the freezing level, this will be a very wet snow with potential to down power lines and trees.

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