The hows, whys, and whens of weather forecast models
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There are few among us who remember a time without weather forecasts. That well-groomed, resplendent TV presenter pointing to charts and squiggly lines, complete with oddly placed coloured triangles, has become a staple of western society.
More recently, we turned to the Web for our forecasts. But where did the modern weather forecast originate? And where is it created now?
Lewis F. Richardson and his computers
In 1922, a man named Lewis Fry Richardson become my personal hero. He achieved this very distinguished status despite the fact that he failed so spectacularly at the very thing he is famous for. In 1922, a "computer" was a person who knew mathematics and had access to a pen and some paper. Richardson was in possession of both.
He was also a very talented mathematician and meteorologist, and it occurred to him that it might just be possible to use the equations of motion, for which we have Isaac Newton to thank, to predict the weather.
Think of it this way: if I have a ball and I apply a force to it, say, I throw it directly forward, will I be able to "predict" where it will go? You bet I can. (It'll fly forward, duh.) So why not do this with air? All you need do is figure out what forces are at play.
And so Richardson grabbed his pen and paper, and had a try. He figured out all the forces a parcel of air might experience, and stepped the location of the air forward in time.
As an example, he used a domain over western Europe and tried to make a weather forecast of pressure that spanned a 24 hour period. This was the first "numerical" weather forecast. The math of this one forecast took him two years to complete.
Richardson predicted a rise of 145 hPa in 6 hours, a result which is basically laughable, most likely souring his dream of having a warehouse full of computers (people with pens and paper), who would pass notes around to construct a forecast faster than the speed of the evolving weather.
The thing is, Richardson's method was correct; he was just missing a critical piece of the puzzle.
My old meteorology professor in Ireland, Peter Lynch, wrote a book about Richardson's forecast. He remarked that Richardson neglected something called "initialization". Without it, it's like trying to predict ocean tides by using waves. You need to smooth out the high-frequency stuff before you can see the true signal.
Charney, and the scariest maths you ever saw
By the 1950s, things were coming together. The initialization problem had been solved by some extremely smart people, such as Jules Charney and John von Newmann, who derived the quasi-geostrophic potential vorticity equations (which I can barely spell, much less understand).
By then, the first true supercomputer, known as ENIAC, was constructed by the Americans. The new "numerical weather forecast" was the ideal problem for giving ENIAC a test drive.
ENIAC's forecast went well, and numerical weather prediction (NWP) was born. The ingredients were all there: initialization had been solved, the equations of fluid dynamics had been discretized (split up into chunks of time, called timesteps), and the computational power had finally become available.
NWP today
Casually brushing over the following 70 years of in-depth NWP research, let's skip forward to today. What does Richardson and Charney's legacy look like now? Windy is cutting-edge in the field of NWP visualization, but try to remember that Windy is not an NWP institute.
There is maybe one NWP institute in each of the world's most developed countries – notably the US, UK, Japan, Canada, France, Germany and Norway. These are often government run, but private organisations are beginning to develop their own in-house models now as well (notably Meteoblue and IBM).
If you've used Windy for a while, you've probably heard of ECMWF. I've done courses/conferences at ECMWF, and these visits have helped to convince me that they're the best in the world at what they do.
ECMWF (it stands for European Centre for Medium-range Weather Forecasts) was incepted in 1975, as a collaborative effort throughout most of Europe (18 countries).
They're based in Reading, a suburb of London, but soon they'll move elsewhere in Europe (possibly Italy). ECMWF is a modelling centre, and its model is known as the IFS (Integrated Forecast System) — though many just know it as "the ECMWF model".
The UK Met Office are also world leaders when it comes to weather modelling – their model is known as the UM (Unified Model). The UM is second only to the IFS in its accuracy (though the accuracy rankings are always changing). The Met Office's UM partnership extends across the world, allowing UM access for many nations including Australia, New Zealand, South Korea and India. It's costly data though, and commercial UM access is prohibitively expensive.
Not be left behind, the American NOAA (National Oceanic and Atmospheric Administration) is also a world leader in NWP, with its flagship model the GFS (Global Forecast System). One important point of note about the GFS is that its output is completely free of charge. You don't even need to sign-up to grab the GRIB files from their server.
Ever wonder why there are so many weather apps? Free data for all. Not just that – you can literally sell this data. You can download NOAA's data, make it pretty, and actually sell it. All you need is enough coding knowledge to read a GRIB file and make a website. Some people do this better than others...
And lest we forget HIRLAM, AROME, ICON, CAM, WRF, NEMS, GRAF, and the whole host of other state-of-the-art NWP systems produced by organisations across the world. These days, we're spoiled for choice. But it's important not to forget the resources it takes to create a weather model, much less a competitively accurate one.
So, what's the lesson here?
If you want to get a little better at understanding a weather forecast, the first thing to do is to pay attention to where the data came from. Did it come from NOAA? ECMWF? Meteoblue? Each model will run at a different resolution, sure, but resolution isn't everything.
The skill of the scientists who coded up the model comes into play, and the approaches taken to every component of the model will matter immensely. Are you using a model in the US that was tuned to work best in the UK? Is ECMWF the best model for everyone, or does ICON actually perform better in your location? By how much do the model forecasts disagree on any given day?
On Windy, a good start is to play around with the different models. At the time of writing, the available models are the Swiss NEMS (uses machine learning AI, which is cool), France's AROME, Germany's ICON, NOAA's GFS (and NAM, a North American nest domain), and ECMWF's IFS. Some of these are "global" domains, and some are "limited area models (LAMs)", but I think I'll leave these concepts for a future article.
It's a tricky business, and it can be overwhelming for the average weather user. Many will simply look at ECMWF and that's good enough; it depends on what you're using the forecast for.
There are more advanced strategies in modelling such as ensemble forecasting (a technique that tries to get a handle on the chaotic nature of weather), but observing things like "postage stamps" and "spaghetti charts" may not be helpful to the uninitiated.
If you're interested, and bear in mind that I'm heavily biased, numerical weather prediction is a rabbit-hole that will leave you more and more fascinated the deeper you go.
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@johnckealy
Weather forceast model is a fascinating topic ! The more you use them the better you understand them, you know when one model can be more accurate than another, what model can or cannot do. IFS is probably one of the best model to predict the changes at synoptic level (1000 or 2000km) when Arome is capable to solve and calculates convection which can be useful to predict thunderstorms.Of course we can keep using one model only but Windy offers the possibility to easily compare the results from the different models so it is a nice and easy way to check the consistency between the models and therefore give you more confidence about the weather forecasts.
