The Sources of Errors in Weather Forecasts

Why is it so difficult for a supercomputer model using tens of millions of weather observations from satellites and radars and weather balloons to forecast the weather correctly in the first place? There two major sources of errors in weather forecasts, "chaos" and "model error." The longer the forecast, the larger these errors become.

Chaos is a mathematical buzzword describing the property that errors tend to grow exponentially in certain systems of equations. The evolution of the weather can be described with a very large set of equations, and numerical weather forecasts exhibits this chaos. Practically, what this means is that if a weather feature at is not described perfectly at the start of the numerical forecast, the initially small error will grow very rapidly and eventually ruin it. As the classic example goes, suppose the current state of the weather were known perfectly except for one unaccounted-for flap of a butterfly's wings in Asia. Incredibly, that initially small error in the computer representation of the weather can grow fast enough to render the forecast over the U.S. two weeks later nearly useless.

Another reason that weather models cannot make accurate medium-range weather forecasts is that the models themselves are imperfect codifications of the laws of nature. Although there are millions of equations for each model, there still is not enough computer resources available to represent all the small-scale details. The computer model of the weather thus treats the weather over you as being the same as the weather a block down the road. Further, all those little details -- how the wind is slowed blowing around your house, or how much water evaporated from the pond down the road -- these are only treated approximately, averaged over many houses and many ponds. Without a perfect description of every house and every pond, "model errors" are inevitably introduced.