At last, we come to a weatherman. Weather forecasters hold the dubious honour of being so well known for getting it wrong that it has become a cultural cliché, so it only seems right that we include at least one in our entries. And there’s really only one contender: the UK’s Michael Fish. On the 15th October 1987, during a regular forecast, Fish mentioned that a caller had asked about the possibility of a hurricane hitting that evening. He assured viewers not to worry, and that there wasn’t one on its way. Hours later, a hurricane hit, in what came to be known as The Great Storm. It was the worst recorded storm in Britain for hundreds of years, and caused tremendous damage as well taking 22 lives.

Great-Storm
Image attribution BRG2

Similar to the misplaced confidence in the build-up to the financial crash, this is another prediction that only made it worse when the opposite came to pass. Believing the forecast, many didn’t take adequate precautions or make suitable preparations. It also led to the ‘Michael effect’, whereby BBC weathermen now take care to outline the worst-case scenario. And the actual status of the prediction is up for some debate – at first Fish claimed that he was actually talking about a call from a Floridian woman, who was worrying about a hurricane in Florida. Then he said he simply made the caller up. So who are we to judge?

The stereotype about weather forecasters is, of course, very unfair. Weather forecasters do a remarkably good job with the tools and data available, given the sheer difficulty and immensity of the task – and are only getting better at it. Weather is a prime example of a highly complex, chaotic system, with innumerable variables interacting to produce outcomes that are very difficult to predict accurately. Yet the science of weather forecasting has made tremendous strides, thanks in part to advances in numerical forecasting, which involves applying mathematical models to trillions of observational data points collected from around the world. It’s tremendously difficult to do, but by taking an analytical, data driven approach that incorporates every piece of information – every insight – out there, we have found a way to do it to an extent, and we’re getting better.

Why not try forecasting yourself with the almanis community?

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