Understanding Why Forecasts Bust

In light of the major snowfall bust for the New York City area on Monday this week, many are looking at forecasters and wondering how such a thing can happen. Technology has advanced to the point where we can have semi-accurate forecasts up to 7 days in advance, but some of the bigger systems are much harder to forecast even with our model guidance. Well, the folks at the NWS in Kansas City threw together an excellent graphic explaining this.

The graphic explaining how dynamic meteorological forecasting actually is. h/t NWS Kansas City
The graphic explaining how dynamic meteorological forecasting actually is. h/t NWS Kansas City

Let me simplify this graphic for you. As we have seen, especially here, the atmosphere is incredibly complex. It can be changed at the drop of a dime, and can impacted by very small things (The Butterfly Effect). Because of this, the equations that are used to build our models are long and complicated. To add even more craziness on top of that, the initial conditions and how models handle them vary from model to model.

How many could view the complexity of forecasting. h/t giphy.com
How many could view the complexity of forecasting. h/t giphy.com

This creates layers of complexity on top of an already complex situation. Add into that the variability of the systems that we forecast before the big system is supposed to hit, and the differences in forecaster interpretation, and you get a very tough forecasting scenario.

Forecasting 75ºF and sunny is generally an easy call. But when you have all of these thing going into a big system, it can create chaos and challenging forecasts for meteorologists. Nevertheless, meteorologists are trained to handle any situation that the atmosphere can throw at them. When model guidance and experience/knowledge of the atmosphere are blended, the forecast will usually come out okay!

h/t giphy.com
h/t giphy.com