1. Outline for Today Brief Lecture on weather forecasting A comment on the upcoming Exam 4 and a bunch of other stuff

2. Theoretical Models and Accuracy As we discussed previously, one of the most useful ways of evaluating a theoretical model is to compare the predictions of the model against empirical data Unfortunately, many industries do not provide you with information on the accuracy of their predictions –how many people improve after taking a drug –how many people actually lose weight by eating Subway sandwiches –how many horoscopes accurately predict the future As a consequence, it is almost impossible for you, as a consumer, to evaluate the quality of the product

3. In general, weather forecasters make their predictions based on either mathematical models/simulations of weather systems, past weather conditions, intuitive judgments based on the movement of current weather systems, or “all of the above”

4. Unfortunately, weather forecasters do not tell us—the consumers—how accurate their forecasts are. We don’t know what the “track record” is for any weather/news team. (Moreover, they don’t keep records of past forecasts on their websites, thereby preventing consumers from investigating the matter themselves.)

5. A scientific analysis of the accuracy of weather forecasting Students recorded the 5-day forecasts (on Sunday nights) for 3 weeks for the following sources –WGNTV –NBC –CH. 7 –Fox News –Intellicast –Chicago Tribune I recorded the actual, end-of-the-day weather from an unrelated source: Weather Underground. This allows us to compare the predictions made by the different teams with the actual weather.

6. Average temperature observed, as recorded by Weather Underground, Chicago April 5 th – April 23rd

7. DatePredictedObservedDiscrepancy 4/5 46.0041.005.0 4/6 48.5058.00-9.5 4/7 43.5054.00-10.5 4/8 38.0050.00-12.0 4/9 38.0046.00-8.0 4/12 35.5038.00-2.5 4/13 43.0040.003.0 4/14 47.0050.00-3.0 4/15 53.5062.00-8.5 4/16 55.0070.00-15.0 Average absolute discrepancy: 7.7 degrees ~ not too bad, eh? ~ Predictions made by WGN, Channel 9, on Sunday nights

8. Errors tended to get larger as more days passed between when the prediction was made (Sunday evenings) and the day in question. Five days later, the average prediction was off by about 10 degrees. (These numbers are based on WGN TV) Number of days since prediction Discrepancy between predicted and actual temperature

9. WGNTV | Mean Discrepancy = 6.3 NBC | Mean Discrepancy = 4.2CH. 7 | Mean Discrepancy = 7 Fox | Mean Discrepancy = 3.9 Intellicast | Mean Discrepancy = 8.9 Chicago Tribune| Mean Discrepancy = 6.2

10. DateObservedPredicted 4/5 41.00 4/6 58.0041.00 4/7 54.0058.00 4/8 50.0054.00 4/9 46.0050.00 4/12 38.0046.00 4/13 40.0038.00 4/14 50.0040.00 4/15 62.0050.00 4/16 70.0062.00 As an additional comparison, we can generate predictions in another way: based on “day before” temperature In this case, we’re assuming that the best bet for tomorrow’s temperature is today’s. Average discrepancy: 7.35

11. What about broader weather conditions? Broader weather conditions –(a) sunny/clear –(b) partly cloudy/mostly cloudy –(c) thunderstorm –(d) rain/showers Accuracy in prediction –NBC73% –Intellicast80% –Chi. Trib40% –Day before71%

12. Summary It appears that the various weather teams do a pretty good job in predicting the temperature –They were only off by approximately 4 – 9 degrees, on average. –Their ability to make accurate forecasts was lower for extended forecasts. In predicting weather conditions (e.g., rain, shine), the various stations were right 40 (Chi. Tribune) – 80% (Intellicast) of the time. Nonetheless, you wouldn’t know how good they are without doing the research yourself.