I've always thought of TV weather forecasters as just talking heads. Sure they look pretty, waving hands in front of fancy green-screen graphics, reading poetically off the teleprompters, and standing fearlessly in the midst of the worst storm conditions. But could we expect man candy as tart as Al Roker and Willard Scott to actually know anything about science and math?

Well, maybe not Al and Willard. But Greg Fishel, Chief Meteorologist at WRAL in Raleigh, is bringing the goods.

In a recent post on the WRAL WeatherCenter Blog by Nate Johnson, Fishel is interviewed on the topic of ensemble forecasting. This 10 minute video is worth a look.

Deterministic vs. Ensemble Weather Forecasting Models

First Fishel describes the traditional "deterministic" weather model. In this approach, observered initial conditions (temperature, pressure, etc., from various observation points) are fed into a computer model. These initial conditions provide the current state of the atmosphere, from which the model derives the state of the atmosphere at some point (e.g. 7 days) in the future.

Everyone realizes that we can't expect a perfect prediction of next week's weather, which is what the deterministic model purports to deliver. In fact, we don't even have perfect knowledge of the current state of the atmosphere, since we have only a finite number of weather monitors reporting conditions at particular locations.

The ensemble approach, as Fishel explains, takes the initial condition data, and perturbs the data points (e.g. slightly changing the temperature and pressure at each point, in various ways), creating an ensemble of perhaps 50 sets of initial condition data. Each variation of initial conditions is run through the same model, and the resulting solutions are compared.

If all versions give essentially the same result a week out, this would imply that the atmosphere is not overly sensitive to small variations in initial conditions, and this would merit more confidence in the forecast.

If the different versions of input data resulted in wildy different forecasts, we might have much less confidence in our weather prediction.

Application to Business Forecasting

What is happening here, which is a very good lesson for business forecasters as well, is acknowledgment that it is impossible to make a perfectly accurate forecast. As Fishel puts it, "I don't think there is anything wrong, in a highly uncertain situation, to be honest with the public and say 'we don't know how this will play out, but here are the most likely scenarios.'" Then as a consumer of the weather forecast, you can plan accordingly.

An indication of uncertainty is a valuable addition to the typical "point forecast" that just tells us one number. For example, telling your inventory planner that the demand forecast is for "100 +/- 100 units" might lead to a different inventory position than a forecast of "100 +/- 10 units."

So forget about Al, Willard, and the long list of celebrities* who served up the weather at some point in their careers. Not only is Greg Fishel good looking enough to land a nightly gig on a mid-market CBS affiliate, he can teach us a thing or two about forecasting!

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*Including David Letterman, Pat Sajak, and Raquel Welch.