Weather Forecasting - A New Idea

Do you think better weather forecasting requires a degree in meteorology? Maybe a degree in statistical analysis would be more helpful. Consider this following new way to forecast the weather with more accuracy and less knowledge.

Friday, February 2, 2007 - Canon City, Colorado. I brought in my Canon City Daily Record from the porch when it arrived, at about 3 in the afternoon. I opened the newspaper to the page with the weather forecast, wondering how cold it would be the following day.

The paper forecast a high temperature was 13 degrees Fahrenheit. I knew this was way too low. Forecasts on television and on the internet said that we would reach 23 or 27 degrees the following day. I knew they were also too low, and I told my wife it would be in the 30s at least. The actual high temperature the next day was 53 degrees Fahrenheit.

No, that's not a typo. The various weather forecasting "experts" were off by as much as 40 degrees - and that was for a simple 24-hour forecast. How could they be so far off? And how could I be better than them at forecasting the weather?

I can't answer the first question. The weather here is more unpredictable than in most places. Also, perhaps the meteorologists follow there computer models too slavishly, even when their experience and intuition tell them to adjust the forecast.

However, I can answer the second question. My guess was closer because the forecasters were so consistent in the way they made their errors. Around this time, I remember counting something like 15 out of 20 days when all the various weather forecasts predicted a high temperature that was 5 degrees or more too low. All I needed to do was take the highest temperature forecast and add five degrees.

A New Weather Forecasting Model

Consistency in their errors was the key to my success. They weren't forecasting too high one day and too low the next. They were wrong in the same ways over and over.

Are the errors as consistent in other parts of the country? That could be determined by looking at the statistics. Check the forecast highs and lows for the last 365 days, and check the actual temperatures for those days. See what the predicted probabilities of rain or snow were, and what actually happened.

Suppose that of the last 24 times a forecaster predicted a 50% chance of rain, it actually rained 18 times. He may have the best data, but he may be too conservative in how he uses it. Suppose this was not a fluke - which can be determined by doing more statistical analysis. You could know nothing about weather forecasting and provide a more accurate forecast simply by saying "A 75% chance of rain tomorrow" every time he said there was a 50% chance, right?

That's the basis for this new forecasting model. First gather the statistical information on the forecasts of several weather forecasting services or meteorologists. Compare this to the actual weather that happened, and look for any consistencies in the inaccuracies. Then you create a computer program. As you enter each of these forecasts into it, they are adjusted for known tendencies. The result is a more accurate forecast.

If Forecaster A has managed over the last year to forecast a high that averages 4 degrees over the actual high, the computer adjusts for that. More sophisticated analysis might show that Forecaster   B is consistently predicting a higher probability of rain than there is in the fall, but a lower probability of rain than there actually is in the spring. The computer can take this into account. Finally, it may work best if the adjusted forecasts of three or more sources are then averaged.

There really is no need to know anything at all about weather forecasting. This is based on the idea that even when experts have all the best knowledge and data, they sometimes apply it incorrectly, and do so in consistent ways. Don't be surprised if some television stations get rid of their meteorologists and take advantage of this new weather forecasting idea.

"Now your electronic weather forecast, from our Statistical Analysis Weather Machine."