“They get it wrong 90% of the time”

I was on a long train journey a few days ago, and ended up in conversation with the person next to me. When I explained what I’m doing (a PhD project looking to improve sub-seasonal forecasting), I was greeted with the all-too-familiar response of “oh, that’s good because they get it wrong 90% of the time”. Doing my utmost to supress just how much that sentence annoyed me with its factual inaccuracy, I responded with a comment about how there is still so much to learn about the atmosphere-ocean system, and ‘things can only get better’ (perhaps that song by D:Ream should be the soundtrack to NWP – or is it still too tainted by Tony Blair?). I also recently saw a post on Reddit’s ‘showerthoughts’ saying “Forecasting the weather is the only job where you can get it wrong every day and still have a job” – I refrained from responding to that!

Of course, “they” don’t get it wrong 90% of the time (you could easily argue the reverse is true) and a forecaster won’t still be in the job if they’re getting it wrong every day.

In the last few days we have seen a spectacular example of what NWP is capable of with the forecast of Hurricane Florence. We knew days in advance that the storm would reach category 4 status. The track into the Carolinas is now without doubt (though the exact motion when the storm makes landfall remains uncertain). So how is it that a forecast of a huge, turbulent, dynamic vortex can be so incrediby accurate to an extent that even amazes meteorologists – and yet the general public can have such a different opinion about their experiences of weather forecasts? How is it that this perception is so ubiquitous?

I had noticed over the summer that there were noticeable issues with app forecasts – and noticeable failures – but there is often good reason. Showers are difficult to predict to postcode-level accuracy, and, sometimes, “shit happens”. A great example of the latter occurred earlier in 2018 in Reading, in perhaps the only time I’ve really experienced the forecast go completely wrong. The forecast: a dry, cloudy day. However, the inversion mixed out, resulting in clear, sunny skies. That in turn lead to unexpected solar heating and thus unexpected instability, which generated a heavy (but very isolated) shower over Reading (perhaps its localised nature was a response to additional urban heating – yet another complexity!). A wonderful non-linear response – and something which as a meteorologist made sense. However, to a member of the public, it was unexpected rain that may have left them irritatingly soaked – and perhaps fostered a resentment of weather forecasters.

Forecast accuracy has come on in leaps and bounds over the last 30 years, but it seems public’s trust has not increased accordingly. I think part of this is how quickly one adjusts to a ‘new normal’ – I can draw a parallel to Internet connection speeds (remember when 1 Mbps was fast, yet now seems painfully slow?). I think a large part comes down to a lack of understanding as to why a forecast may go wrong. Rather like a medical diagnosis, it may make logical sense as to why a Doctor misdiagnosed, but the patient’s response may be filled with anger and confusion. And part of that comes down to the human body seeming naiively simple (because we all have one!) in the same way forecasting the weather may seem simple (just some fancy graphics and looking at clouds, right?). Both are hugely complex, but its usually only the experts who truly comprehend that.

Thus, it’s my conclusion that the more meteorology we can get out there, the better the public will trust the forecasts we make.

Edit: after writing this post, I received a wonderful tweet which showed that, for some, the incredible accuracy of weather forecasts is understood.
 

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