Data Intelligence, Business Analytics
For places such as the Pacific Northwest, UK or Germany, where weather is highly volatile, I believe that my mentor's model (when I did my PhD) might actually be the best one. In places where weather is easy to predict by looking at satellite images, you might be able to do well without any model.
The model described below is based on data from the Puget Sound (Seattle) area. For many years, weather forecasts for the Seattle area have been notoriously erroneous. They've been more often wrong than right, resulting in schools not trusting weather forecasts to decide on whether closing or not. This in turn has caused many problems.
This week forecasts is a culmination on erroneous forecasts. Mon-Tue were supposed to be light snow days, and Wed to be the "snowstorm of the century". Indeed, nothing happened Wed and we got a fair amount of snow Mon-Tue.
To make things worse, the meteorologists from University of Washington claimed they were absolutely certain about their forecasts, and had a perfect understanding of what was going to happen. Even on weather.com, you could read "severe storm warning", "100% chance of precipitation", "up to 14 inches of snow", "heavy snow", "100% chance of snow", despetite the temperature being very close (just a bit above) 32 degrees. No confidence intervals of any kind were provided.
So let's now discuss my mentor's model, who also holds a PhD in statistics and has studied in Cambridge and Australia. The model is as follows: tomorrow will be the same as today.
Since this model is better than what meteorologists can do, are they only there for entertainment?