Weather forecasting explained

Explaining why weather forecasting becomes far less accurate further into the future.

Posted: Nov 29, 2017 5:37 PM

Why don't reputable meteorologists focus on daily weather beyond 7 days? Most folks understand that weather becomes less predictable into the future. Have you ever wondered why? Why can't meteorologists have their own version of the Farmer's Almanac, making daily predictions months into the future?

The simple answer is that small mistakes in the short term lead to large mistakes in the long term. This concept is related to the chaos theory, which explains why weather forecasting becomes less predictable further into the future.  Meteorologists can measure predictability by measuring the forecast against climatology (average weather).  If the forecast is less accurate than using average weather, there's zero skill!

Our forecasting models have ways to determine the uncertainty of weather patterns.  One tool we use is called a spaghetti plot. It's a combination of several different computer model runs using slightly different initial conditions. It's a method used to attempt to understand what patterns are predictable and what patterns are not predictable. Here's an example of a spaghetti plot model:

http://mp1.met.psu.edu/~fxg1/ENSHGTAVGNH_12z/ensloopmref.html

If you scroll from left to right you can see how the map becomes much more noisy into the future. Close lines indicate high certainty in the forecast, lines far apart indicate low confidence in the forecast.

There's been debate in the meteorology community as to whether or not this information is useful to the general public.  This type of forecasting is called probabilistic forecasting, and it is quite different than what is used today.  The daily high temperatures we forecast is called deterministic forecasting, and it takes away any concept of uncertainty.  If we followed a probabilistic forecast, we would give ranges in temperature based on uncertainty.  Sometimes the range would be 3 degrees, sometimes it would be 10 degrees.  It's a great concept in the academic field, but it doesn't test well in the real world.

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