Weather Forecasting – a New Concept

by Eric of Absurdity

Post by Steve Gillman

You might believe that weather forecasting requires a degree in meteorology. Properly, perhaps a degree in statistical analysis would be far more beneficial. Here is a new way to forecast the climate with less knowledge, but greater accuracy.

Here in Canon City, Colorado, on Friday, February two, 2007, I brought in my Newspaper from the porch when it arrived, at about 3 in the afternoon. I opened the newspaper to the page with the weather forecast. I was wondering how cold it would be on Saturday.

13 degrees Fahrenheit was the high temperature forecast for Saturday. This was way too low, I figured. I checked the forecasts on television and on the internet. They said we would reach 23 or 27 degrees the following day. These forecasts had been also too low, I knew. I told my wife it would be in the 30s or higher. What was the actual high temperature the next day? 53 degrees Fahrenheit.

That is not a typo, by the way (how do they keep their jobs?). Weather forecasting “specialists” were off by as a lot as 40 degrees – for a straightforward 24-hour forecast of the high temperature. Why had been they so far off, and how could I be better than them at forecasting the climate for Saturday?

Well, I do not have an answer for the 1st component of the question. Weather here is certainly more unpredictable than in several places. Perhaps the meteorologists follow there pc models too slavishly, even when expertise and intuition tell them to adjust a forecast up or down.

I can answer the second part of the question. My forecast was closer because the “authorities” were so consistent in the errors they made. I had counted a thing like 15 out of 20 days when all the numerous climate forecasts predicted a high temperature that was five degrees or much more lower than the actual temperature. Seeing that, all I had to do was take the forecast (the one predicting the highest temperature) and add 5 or six degrees.

A New Climate Forecasting Idea

The consistency in the direction of their errors was the important to my much better forecast. In other words, they weren’t forecasting too high one day and too low the next. They were producing their errors in the exact same methods repeatedly.

The next logical question is regardless of whether errors are as consistent in other parts of the country. Looking at the statistics could answer this. 1 could check the forecast highs and lows for the last 365 days, and check the actual temperatures for those days. 1 could also see what the predicted probabilities of rain or snow had been, and then note what in fact occurred.

For example, suppose a forecaster predicted a 50% likelihood of rain 24 occasions, but it really rained 18 occasions. Possibly he had the greatest data, but he was too conservative in its application. This may possibly not be a 1-time issue. This can be determined by performing far more statistical analysis. If his error was consistent, you could know nothing about weather forecasting and provide a a lot more accurate forecast merely by saying “A 75% chance of rain tomorrow” each time he stated there was a 50% opportunity.

That’s the essence of how this new forecasting idea works. You initial gather statistical details on the forecasts of a number of meteorologists or climate forecasting services. Then you compare these forecasts to the actual weather that occurred, and look for any consistent errors. Ideally you would want to generate a personal computer plan, the concept getting that as you enter each of these forecasts into it, they are adjusted for any identified tendencies. The result should be more accurate climate predictions.

An example may make this clearer. Suppose that more than the last year Forecaster A has been forecasting a high temperature that averages four degrees over the actual high. The laptop or computer would adjusts his forecast down four degrees. Perhaps a much more sophisticated analysis shows that Forecaster B is consistently predicting too high of a probability of rain in the fall, but too low of a probability of rain in the summer. Once this is found and programmed in, the laptop or computer can adjust the forecast for these variables. For higher accuracy, the adjusted forecasts of 3 or a lot more sources could be averaged.

You wouldn’t want to know anything about climate forecasting. The underlying idea is that even when specialists have the greatest knowledge and data, they can apply it incorrectly, and do so consistently. Possibly some tv stations will soon get rid of their meteorologists and take advantage of this new climate forecasting thought: “And now it’s time for your electronic weather forecast, from our Statistical Analysis Climate Machine…”