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I heard people say that assuming tomorrow's weather will be the same as today's is as good as, or better, than meteorological models.

  • This book says people assume is the best way

  • This site claims that it's 40% accurate.

Has anyone tested the accuracy of this model and compared it to modern weather predictions?

  • How accurate it can be would be interesting to see, but both me and likely a lot of other people have seen weather shift rather fast. I've had one point where it rained and was cold the first day, sunny and summer warm the other... – Sharain Mar 29 '15 at 13:00
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    @Sharain: Yes, anecdotal data like that is not terribly helpful when the claim is that it is (only) 40% accurate. – Oddthinking Mar 29 '15 at 13:27
  • @Oddthinking the claim is also that it is (or was) more accurate than weather models. – Sklivvz Mar 29 '15 at 14:19
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    Accuracy in % is not necessarily the most useful metric in weather forecasting. A meteorologist in Saudi Arabia might correctly predict the weather with this model 364 days per year. What makes him (or her? not sure in SA) valuable is predicting the one day that the storm strikes. It also depends on the precision of the forecast. Also, the accuracy of a "tomorrow equals today" model is going to vary tremendously on location. – gerrit Mar 31 '15 at 15:22
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    @gerrit: It's also helpful to understand just why this seems to be a good model, when in fact it isn't really useful. In many places, the weather comes in systems that take several days to pass any given location. For instance, here in the western US, a ridge of high pressure can settle in for many days, bringing clear skies & warm temperatures; or Pacific storm systems may take days to pass through. Thus there will be many more 'similar' days within a system than days which transition between them. (See any meteorology text for references.) – jamesqf Apr 2 '15 at 21:33
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Blogger Randal Olson reproduced a chart from Nate Silver's The Signal and the Noise which in turn has based on data from ForecastWatch.

enter image description here

Ignore the orange line; it is irrelevant for this discussion. (Just for illustration: It is based on a similar idea of predicting that it will be hot on your birthday, because it has been hot on your previous birthdays.)

The blue line represents Persistence - the concept in the question.

The grey line represents commercial quality forecasts.

The higher the line, the worst the estimate.

The blue line is always higher than the grey line - a delta of about 2.5 °F (about 1.5 °C) after 1 day.

Based on this, we can conclude that, although Persistence isn't a terrible model (predicts with an error of only about 5.5 °F, or 3 °C), it performs much more poorly than a professional weather estimate on temperature forecasts.

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    Are forecasts by organisations like NOAA "commercial forecasts"? – gerrit Apr 2 '15 at 15:03
  • Also, note that this considers only temperature. For most people, precipitation and wind are very much part of weather, and the quality of the "persistence" model might be even worse there. – gerrit Apr 2 '15 at 15:07
  • @gerrit Do non-sailors care much about the wind? Assuming the temperature is 'seasonable' I would mostly only want to know whether it's going to rain: and the "persistence" model might be quite good there -- "no rain today and no rain tomorrow" or "rainy today and rainy tomorrow". – ChrisW Apr 2 '15 at 15:18
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    @ChrisW In The Netherlands, almost everybody cares about the wind, because if it's windy you might have to leave home 10 minutes earlier for getting to work on time (by bicycle). So it depends on the area. – gerrit Apr 2 '15 at 16:18
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    Misleading unless you read the axis? Huh? What are we, illiterates? Would you say a graph comparing prices is also misleading if the cheapest alternative is the line furthest down? – gerrit Apr 2 '15 at 17:19
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It is not possible to answer this question as asked because:

  1. What do you mean by weather? Rainfall? Temperature? Wind?
  2. Where in the world are you talking about - different places have more or less climatic variation.

However, just for fun, I downloaded the rainfall data for the Canterbury Racecourse Automatic Weather Station from the Australian Beureau of Meterology (here) and analysed just the rainfall data.

There were 248 days where the previous days rainfall (yes/no) was the same as the current day and 101 that were different, an accuracy of about 71%.

This only adds up to 349 not 364 because:

  1. January 1st has no comparison day in the data set
  2. There were 8 days when no measurements were taken - because these were scattered this led to 14 days where no comparison could be made.

You can do this for any place where such data is available.

  • Please read the notice above and fix your answer accordingly. 1. You need to show your calculations have general validity; 2. You are not comparing the value you found to the predictions, anyways; 3. Avoid any "this is not possible to answer" comments in an ...answer. If it's not possible to answer, suggest a fix in a comment, not an answer; if it's possible to answer, then answer, but don't comment :-) – Sklivvz Apr 2 '15 at 9:38
  • There is actually a lot of research into this topic already, check Markov Chains and autoregressive models. – gerrit Apr 2 '15 at 21:39

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