by The Great Storm of 1987 not only resulted in the loss of 15 million trees. It shook the nation’s faith in weather forecasting. After all, who can forget weatherman Michael Fish infamously telling viewers worried that a hurricane was on the way: “don’t worry, there isn’t!”
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 up to six weeks ahead.
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 drawn from a wider range of sources than ever before, then push it through supercomputers.
For anyone gambling on a sunny wedding these forecasts can’t come soon enough. But the benefits will go beyond last minute holiday bookings, barbecues and party planning. It will change the way industries such as agriculture, fishing and energy operate, and inform decision-making around the world to help governments protect lives and livelihoods.
“Everything we do aims to protect life and property and have a thriving economy,” says Dr Florence Rabier, Director General of the European Center for Medium-Range Weather Forecasts.
For anyone who gambled on a sunny wedding, this news couldn’t come soon enough. However, weather forecasters have felt our pain, explains Rabier.
“We’ve been doing these monthly forecasts for 10 or 15 years, but they’re gradually getting better. We have more capacity with the computers now. We can go to a finer scale, making more observations. And we now have the ability with machine learning to make better forecasts.”
Indeed, things have certainly come a long way since the Meteorological Office was established in 1854. It was founded by Vice Admiral Robert Fitzroy (captain of HMS Beagle during Charles Darwin’s famous voyage) to understand marine climates. improve and therefore improve marine safety. life and property at sea, planting the seeds of climate science used today. The first storm warning service at the Met followed in 1861 (eventually known as the shipping forecast), as did the first public weather forecasting service, a busy year for the new organisation.
In those early years, data collection was hard and basic work. In 1877, plans were drawn up for an observatory at the summit of Ben Nevis, and in 1881 the hardy meteorologist Clement Wragge climbed to its 4,400ft summit every day during the summer months to take measurements (including atmospheric pressure and wind speed and direction) before the observatory was staffed year-round.
In 1922, the advancement of numerical weather forecasting brought mathematics and physics into the forecasting equation – effectively using the study of fluids to better understand the oceans and atmosphere. The first Met Office computer, acquired in 1959, touched on the mathematics behind this field of study, revolutionizing weather forecasting forever.
The availability of satellite data was another step change. In 2010 another step came, when a larger range of computers were used. Then, two years ago, it was machine learning. “This was when we realized that we could use past forecasts and analysis along with the information we have now,” says Rabier. It is vital that the weather data we have already collected is central to our understanding of likely future events.
Average day and ten year forecasting has improved, meaning that in 2020 we were four days better off than in 1980, meaning that the forecast accuracy at that time was three days seven days now.
Today massive streams of data are collected from all over the world, from deep-water buoys that warn of incoming Atlantic storms, automated weather stations that sweep across the countryside, weather balloons, transponders on aircraft and ships and satellites.
The new programme, which will be based at the University of Reading, will use even better data sources. “We are trying to explore things that develop on earth a little more slowly than the atmosphere,” explains Professor Pier Luigi Vidale, Professor of Climate System Science at the University of Reading and senior scientist of the National Center for Atmospheric Science (NCAS).
“The oceans give us a much better understanding of how they transport heat from the equator to the pole and influence the ways in which storms develop and bring winds and rain to our shores. That will also help our forecasts.”
Another unexplored data set that the team will be drawing on is the cityscape. “Buildings and roads are not included in current climate models, but they can have a profound effect on the weather. Current models cannot distinguish between gardens and parks or concrete and roads.” But heat from hot tarmac is what 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 take advantage of newly digitized old data created through an AI system that analyzes the weather based on historical forecasts dating back to 1940. Currently, AI churning is running concurrently with the data collection. is modern, ie but in the end, both aspects will be together.
When you look at a four-week long forecast right now, you’re looking at a weekly average. “So, for example if I’m going skiing, is there a good chance of snow before I go skiing? For this you could look at the monthly forecast. But you couldn’t look and say it’s going to snow a day before you arrive four weeks from now,” explains Rabier.
Vidale lived in Colorado in the United States for a long time. “People really respect the weather because it can be deadly,” he says. “If there’s a blizzard, people are told not to go out.”
The same is true in Japan, says Vidale, where typhoons and tsunamis can have devastating effects. “Japanese people really look at government apps and warnings. Again, because it’s really deadly there.”
As flooding in the UK increases, long-range forecasting will help authorities work out when the rain will stop, so they can deploy resources accordingly.
It’s also clear to see how more accurate long-range forecasts will benefit the construction and agricultural industries, says Rabier: “The former need to know when they can lay the foundations of a building. In agriculture, if you know when there is dry weather for cutting crops, you can plan ahead and hire extra workers. Or if you can see if a cold spell is coming, you can decide if you should invest in covering your crop.”
As we increasingly rely on renewable energy sources, knowing when there is likely to be more wind, sun and rain is critical to effectively deploying wind turbines, solar panels and hydroelectricity.
But in the tourism industry, more accurate long-range forecasts can be a double-edged sword. The benefits? “Climate change is going to change the tourism industry anyway but, using long-range forecasting, people can see if there’s going to be a heat wave in Spain, and if they still want to go. Or, if there’s a tropical cyclone coming in the Caribbean, you might want to go to the Indian Ocean instead.” Which is all well and good, until you get past the downsides – this could open the door to surge pricing 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 our ability to cry about our bad weather? Perhaps, with all this forethought, planning is overrated. Rabier says she doesn’t personally use the month-ahead forecast. “I look forward to a few days, but I don’t plan my life a month ahead. I am also bound by school holidays. For a regular person it may not be as useful as it is for farmers or people involved in building, or managing a wind farm. For me, I don’t need to plan ahead to have an umbrella.”
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