The result of the Great Storm of 1987 was not just the loss of 15 million trees. This shook the country’s confidence in weather forecasts. After all, who can forget weatherman Michael Fish infamously telling viewers worried a hurricane was approaching: “Don’t worry, there’s no such thing!”
Fortunately, almost 40 years later, scientists are much more confident in their data. A 15-year research program has been launched to provide more accurate forecasts for up to six weeks.
The ambitious £30 million partnership between the University of Reading, the Met Office and the European Center for Medium Range Weather Forecasts will use climate data from a wider range of sources than ever before and then process it through supercomputers.
For someone gambling on a sunny wedding, these predictions can’t come true soon enough. But the benefits will go beyond last-minute holiday bookings, barbecues and party planning. It will transform the way industries such as agriculture, fisheries and energy operate and inform decision-making around the world to help governments protect lives and livelihoods.
Director General of the European Center for Medium-Range Weather Forecasts, Dr. “Everything we do is essentially to protect lives and property and to have a thriving economy,” says Florence Rabier.
For someone walking the gambling tightrope at a sunny wedding, this news couldn’t come soon enough. But Rabier explains that weather forecasters feel our pain.
“We’ve been making these monthly forecasts for 10 or 15 years, but they’re slowly improving. We have more capacity now with computers. By going to a finer scale, we can make more observations. “We now have the capacity to make better predictions thanks to machine learning.”
In fact, things have certainly come a long way since the Met Office was founded in 1854. It was founded by Vice Admiral Robert Fitzroy (captain of HMS Beagle during Charles Darwin’s famous voyage) to improve the understanding of maritime climates and therefore the safety of the seas. To ensure the safety of life and property at sea and to plant the seeds of the climate science used today. The Met’s first storm warning service took place in 1861 (which later became known as shipping forecasting), which turned out to be a busy year for the fledgling organisation, as did the first public weather forecasting service.
In those early years, collecting data was a laborious and primitive task. Plans were drawn up for an observatory atop Ben Nevis in 1877, and in 1881 the intrepid meteorologist Clement Wragge climbed the 4,400ft peak every day during the summer months to take measurements (including atmospheric pressure and wind speed and direction). before the observatory was manned year-round.
The advancement of numerical weather prediction in 1922 brought mathematics and physics into the forecasting equation; Studies of liquids have been effectively used to better understand the oceans and atmosphere. The Met Office’s first computer, purchased in 1959, accelerated the mathematics behind this workspace and revolutionized weather forecasting forever.
With the availability of satellite data came another step of change. Another leap forward occurred in 2010, with computers being used more widely. Then, two years ago, it was machine learning. “This is when we realized that we could use historical forecasts and analysis as well as the information we have now,” says Rabier. More importantly, the weather data we currently collect is integral to our understanding of possible future events.
Forecasts have improved by an average of one day per decade; This means we are four days better off in 2020 than in 1980; This means that the accuracy of three-day forecasts back then now means the accuracy of seven-day forecasts.
Today, huge data streams are collected from around the world from deep-water buoys that warn of Atlantic storms, automatic weather stations dotting the countryside, weather balloons, transponders on planes and ships, and satellites.
The new programme, based at the University of Reading, will use even better data sources. “We’re trying to discover things that evolve a little slower on Earth than the atmosphere,” explains Professor Pier Luigi Vidale, Professor of Climate System Science at the University of Reading and senior scientist at the National Center for Atmospheric Science (NCAS).
“Oceans give us a much better understanding of how they transport heat from the equator to the pole, affecting the way storms develop and bring winds and rain to our shores. “This will also help with our forecasts.”
Another unexplored dataset that the team will leverage is the cityscape. “Buildings and roads are not included in current climate models, but they can have a profound impact on weather. Current models cannot distinguish between gardens and parks, or concrete and roads.” But heat from hot asphalt is the stuff microclimates are made of: “The things we use to build a city can have an impact, and we need to include variables like this in our models.”
The program will also leverage newly digitized legacy data created through an AI system that analyzes weather based on historical forecasts dating back to 1940. But eventually these two aspects will merge.
Right now, when you look at a four-week long-range forecast, you’re looking at a weekly average. “For example, if I’m going skiing, is there a chance we’ll get some snow before I go skiing? For this, you can look at the monthly forecasts. But you can’t look four weeks in advance and say it’s going to snow the day before it arrives,” Rabier explains.
Vidale lived in Colorado, USA for a long time. “People really respect the weather there because it can be deadly,” she says. “If there’s a snowstorm, people are told not to go out.”
Vidale says the same is true in Japan, where typhoons and tsunamis can have catastrophic effects. “Japanese people really follow the practices and government warnings. “I’m saying it again because it’s really deadly out there.”
With the UK experiencing an increase in flood events, long-term forecasts will help authorities determine when the rain will stop and thus allocate resources accordingly.
Rabier says that it is clear how more accurate long-term forecasts will benefit the construction and agriculture sectors: “The construction industry needs to know when they can lay the foundations of a building. In agriculture, knowing when a drought period is due to cut crops means you can plan ahead and employ extra labour. Or if you can see if the cold weather is coming, you can decide whether to invest in protecting your crops.”
As we increasingly turn to renewable energy sources, knowing when there will be more wind, sun and rain is key to the effective deployment of wind turbines, solar panels and hydroelectricity.
But in the tourism industry, more accurate long-term forecasts can become a double-edged sword. Professionals? “Climate change will change the tourism industry anyway, but by using a long-term forecast people can see if there will be a heatwave in Spain and if they still want to go. Or if a tropical cyclone is approaching in the Caribbean then you might want to head to the Indian Ocean.” That’s all well and good as far as the cons go, which could open the door to overpricing during periods of good weather or consequences for holiday insurance.
But of course, this is the nation’s favorite topic of conversation. Where would we be without the ability to whine about our harsh weather? Perhaps, despite all this foresight, too much importance is given to planning. Rabier says he personally doesn’t use the next month forecast. “I look forward a few days, but I don’t plan my life a month ahead. I’m also committed to school holidays. For the average person, this may not be as useful as it is for farmers or people involved in construction or managing a wind farm. “For me, I don’t have to plan ahead to buy an umbrella.”
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