The $2.5 trillion reason we can’t rely on batteries to clean up the grid

Fluctuating solar and wind power require lots of energy storage, and lithium-ion batteries seem like the obvious choice—but they are far too expensive to play a major role.

• by James Temple
• July 27, 2018
• Original link: https://www.technologyreview.com/s/611683/the-25-trillion-reason-we-cant-rely-on-batteries-to-clean-up-the-grid/

If state regulators sign off, however, it could be the site of the world’s largest lithium-ion battery project by late 2020, helping to balance fluctuating wind and solar energy on the California grid.

The 300-megawatt facility is one of four giant lithium-ion storage projects that Pacific Gas and Electric, California’s largest utility, asked the California Public Utilities Commission to approve in late June. Collectively, they would add enough storage capacity to the grid to supply about 2,700 homes for a month (or to store about .0009 percent of the electricity the state uses each year).

The California projects are among a growing number of efforts around the world, including Tesla’s 100-megawatt battery array in South Australia, to build ever larger lithium-ion storage systems as prices decline and renewable generation increases. They’re fueling growing optimism that these giant batteries will allow wind and solar power to displace a growing share of fossil-fuel plants.

But there’s a problem with this rosy scenario. These batteries are far too expensive and don’t last nearly long enough, limiting the role they can play on the grid, experts say. If we plan to rely on them for massive amounts of storage as more renewables come online—rather than turning to a broader mix of low-carbon sources like nuclear and natural gas with carbon capture technology—we could be headed down a dangerously unaffordable path.

Small doses

Today’s battery storage technology works best in a limited role, as a substitute for “peaking” power plants, according to a 2016 analysis by researchers at MIT and Argonne National Lab. These are smaller facilities, frequently fueled by natural gas today, that can afford to operate infrequently, firing up quickly when prices and demand are high.

Lithium-ion batteries could compete economically with these natural-gas peakers within the next five years, says Marco Ferrara, a cofounder of Form Energy, an MIT spinout developing grid storage batteries.

“The gas peaker business is pretty close to ending, and lithium-ion is a great replacement,” he says.

This peaker role is precisely the one that most of the new and forthcoming lithium-ion battery projects are designed to fill. Indeed, the California storage projects could eventually replace three natural-gas facilities in the region, two of which are peaker plants.

But much beyond this role, batteries run into real problems. The authors of the 2016 study found steeply diminishing returns when a lot of battery storage is added to the grid. They concluded that coupling battery storage with renewable plants is a “weak substitute” for large, flexible coal or natural-gas combined-cycle plants, the type that can be tapped at any time, run continuously, and vary output levels to meet shifting demand throughout the day.

Not only is lithium-ion technology too expensive for this role, but limited battery life means it’s not well suited to filling gaps during the days, weeks, and even months when wind and solar generation flags.

This problem is particularly acute in California, where both wind and solar fall off precipitously during the fall and winter months. Here’s what the seasonal pattern looks like:

If renewables provided 80 percent of California electricity – half wind, half solar – generation would fall precipitously beginning in the late summer.

Clean Air Task Force analysis of CAISO data

This leads to a critical problem: when renewables reach high levels on the grid, you need far, far more wind and solar plants to crank out enough excess power during peak times to keep the grid operating through those long seasonal dips, says Jesse Jenkins, a coauthor of the study and an energy systems researcher. That, in turn, requires banks upon banks of batteries that can store it all away until it’s needed.

And that ends up being astronomically expensive.

California dreaming

There are issues California can’t afford to ignore for long. The state is already on track to get 50 percent of its electricity from clean sources by 2020, and the legislature is once again considering a bill that would require it to reach 100 percent by 2045. To complicate things, regulators voted in January to close the state’s last nuclear plant, a carbon-free source that provides 24 percent of PG&E’s energy. That will leave California heavily reliant on renewable sources to meet its goals.

The Clean Air Task Force, a Boston-based energy policy think tank, recently found that reaching the 80 percent mark for renewables in California would mean massive amounts of surplus generation during the summer months, requiring 9.6 million megawatt-hours of energy storage. Achieving 100 percent would require 36.3 million.

The state currently has 150,000 megawatt-hours of energy storage in total. (That’s mainly pumped hydroelectric storage, with a small share of batteries.)

If renewables supplied 80 percent of California electricity, more than eight million megawatt-hours of surplus energy would be generated during summer peaks.

Clean Air Task Force analysis of CAISO data.

Building the level of renewable generation and storage necessary to reach the state’s goals would drive up costs exponentially, from $49 per megawatt-hour of generation at 50 percent to $1,612 at 100 percent.

And that's assuming lithium-ion batteries will cost roughly a third what they do now.

California’s power system costs rise exponentially if renewables generate the bulk of electricity.

Clean Air Task Force analysis of CAISO data.

“The system becomes completely dominated by the cost of storage,” says Steve Brick, a senior advisor for the Clean Air Task Force. “You build this enormous storage machine that you fill up by midyear and then just dissipate it. It’s a massive capital investment that gets utilized very little.”

These forces would dramatically increase electricity costs for consumers.

“You have to pause and ask yourself: ‘Is there any way the public would stand for that?’” Brick says.

Similarly, a study earlier this year in Energy & Environmental Science found that meeting 80 percent of US electricity demand with wind and solar would require either a nationwide high-speed transmission system, which can balance renewable generation over hundreds of miles, or 12 hours of electricity storage for the whole system.

At current prices, a battery storage system of that size would cost more than $2.5 trillion.

A scary price tag

Of course, cheaper and better grid storage is possible, and researchers and startups are exploring various possibilities. Form Energy, which recently secured funding from Bill Gates’s Breakthrough Energy Ventures, is trying to develop aqueous sulfur flow batteries with far longer duration, at a fifth the cost where lithium-ion batteries are likely to land.

Ferrara’s modeling has found that such a battery could make it possible for renewables to provide 90 percent of electricity needs for most grids, for just marginally higher costs than today’s.

But it’s dangerous to bank on those kinds of battery breakthroughs—and even if Form Energy or some other company does pull it off, costs would still rise exponentially beyond the 90 percent threshold, Ferrara says.

“The risk,” Jenkins says, “is we drive up the cost of deep decarbonization in the power sector to the point where the public decides it’s simply unaffordable to continue toward zero carbon.”

Why Renewables Can’t Save the Planet

By Michael Shellenberger

Original link:  https://quillette.com/2019/02/27/why-renewables-cant-save-the-planet/?fbclid=IwAR2FJh18c9OEkcu8xV_c346c5194j8lMZpxSG_CRLLnlcSu9Xnfks1KfnLs 

 

When I was a boy, my parents would sometimes take my sister and me camping in the desert. A lot of people think deserts are empty, but my parents taught us to see the wildlife all around us, including hawks, eagles, and tortoises.

After college, I moved to California to work on environmental campaigns. I helped save the state’s last ancient redwood forest and blocked a proposed radioactive waste repository set for the desert.

In 2002, shortly after I turned 30, I decided I wanted to dedicate myself to addressing climate change. I was worried that global warming would end up destroying many of the natural environments that people had worked so hard to protect.

I thought the solutions were pretty straightforward: solar panels on every roof, electric cars in every driveway, etc. The main obstacles, I believed, were political. And so I helped organize a coalition of America’s largest labor unions and environmental groups. Our proposal was for a $300 billion dollar investment in renewables. We would not only prevent climate change but also create millions of new jobs in a fast-growing high-tech sector.

Our efforts paid off in 2007 when then-presidential candidate Barack Obama embraced our vision. Between 2009–15, the U.S. invested $150 billion dollars in renewables and other forms of clean tech. But right away we ran into trouble.

The first was around land use. Electricity from solar roofs costs about twice as much as electricity from solar farms, but solar and wind farms require huge amounts of land. That, along with the fact that solar and wind farms require long new transmissions lines, and are opposed by local communities and conservationists trying to preserve wildlife, particularly birds.

Another challenge was the intermittent nature of solar and wind energies. When the sun stops shining and the wind stops blowing, you have to quickly be able to ramp up another source of energy.

Happily, there were a lot of people working on solutions. One solution was to convert California’s dams into big batteries. The idea was that, when the sun was shining and the wind was blowing, you could pump water uphill, store it for later, and then run it over the turbines to make electricity when you needed it.

Other problems didn’t seem like such a big deal, on closer examination. For example, after I learned that house cats kill billions of birds every year it put into perspective the nearly one million birds killed by wind turbines.

It seemed to me that most, if not all, of the problems from scaling up solar and wind energies could be solved through more technological innovation.

But, as the years went by, the problems persisted and in some cases grew worse. For example, California is a world leader when it comes to renewables but we haven’t converted our dams into batteries, partly for geographic reasons. You need the right kind of dam and reservoirs, and even then it’s an expensive retrofit.

A bigger problem is that there are many other uses for the water that accumulates behind dams, namely irrigation and cities. And because the water in our rivers and reservoirs is scarce and unreliable, the water from dams for those other purposes is becoming ever-more precious.

Without large-scale ways to back-up solar energy California has had to block electricity coming from solar farms when it’s extremely sunny, or pay neighboring states to take it from us so we can avoid blowing-out our grid.

Despite what you’ve heard, there is no “battery revolution” on the way, for well-understood technical and economic reasons.

As for house cats, they don’t kill big, rare, threatened birds. What house cats kill are small, common birds, like sparrows, robins and jays. What kills big, threatened, and endangered birds—birds that could go extinct—like hawks, eagles, owls, and condors, are wind turbines.

In fact, wind turbines are the most serious new threat to important bird species to emerge in decades. The rapidly spinning turbines act like an apex predator which big birds never evolved to deal with.

Solar farms have similarly large ecological impacts. Building a solar farm is a lot like building any other kind of farm. You have to clear the whole area of wildlife.

In order to build one of the biggest solar farms in California the developers hired biologists to pull threatened desert tortoises from their burrows, put them on the back of pickup trucks, transport them, and cage them in pens where many ended up dying.

As we were learning of these impacts, it gradually dawned on me that there was no amount of technological innovation that could solve the fundamental problem with renewables.

You can make solar panels cheaper and wind turbines bigger, but you can’t make the sun shine more regularly or the wind blow more reliably. I came to understand the environmental implications of the physics of energy. In order to produce significant amounts of electricity from weak energy flows, you just have to spread them over enormous areas. In other words, the trouble with renewables isn’t fundamentally technical—it’s natural.

Dealing with energy sources that are inherently unreliable, and require large amounts of land, comes at a high economic cost.

There’s been a lot of publicity about how solar panels and wind turbines have come down in cost. But those one-time cost savings from making them in big Chinese factories have been outweighed by the high cost of dealing with their unreliability.

Consider California. Between 2011–17 the cost of solar panels declined about 75 percent, and yet our electricity prices rose five times more than they did in the rest of the U.S. It’s the same story in Germany, the world leader in solar and wind energy. Its electricity prices increased 50 percent between 2006–17, as it scaled up renewables.

I used to think that dealing with climate change was going to be expensive. But I could no longer believe this after looking at Germany and France.

Germany’s carbon emissions have been flat since 2009, despite an investment of $580 billion by 2025 in a renewables-heavy electrical grid, a 50 percent rise in electricity cost.

Meanwhile, France produces one-tenth the carbon emissions per unit of electricity as Germany and pays little more than half for its electricity. How? Through nuclear power.

Then, under pressure from Germany, France spent $33 billion on renewables, over the last decade. What was the result? A rise in the carbon intensity of its electricity supply, and higher electricity prices, too.

What about all the headlines about expensive nuclear and cheap solar and wind? They are largely an illusion resulting from the fact that 70 to 80 percent of the costs of building nuclear plants are up-front, whereas the costs given for solar and wind don’t include the high cost of transmission lines, new dams, or other forms of battery.

It’s reasonable to ask whether nuclear power is safe, and what happens with its waste.

It turns out that scientists have studied the health and safety of different energy sources since the 1960s. Every major study, including a recent one by the British medical journal Lancet, finds the same thing: nuclear is the safest way to make reliable electricity.

Strange as it sounds, nuclear power plants are so safe for the same reason nuclear weapons are so dangerous. The uranium used as fuel in power plants and as material for bombs can create one million times more heat per its mass than its fossil fuel and gunpowder equivalents.

It’s not so much about the fuel as the process. We release more energy breaking atoms than breaking chemical bonds. What’s special about uranium atoms is that they are easy to split.

Because nuclear plants produce heat without fire, they emit no air pollution in the form of smoke. By contrast, the smoke from burning fossil fuels and biomass results in the premature deaths of seven million people per year, according to the World Health Organization.

Even during the worst accidents, nuclear plants release small amounts of radioactive particulate matter from the tiny quantities of uranium atoms split apart to make heat.

Over an 80-year lifespan, fewer than 200 people will die from the radiation from the worst nuclear accident, Chernobyl, and zero will die from the small amounts of radiant particulate matter that escaped from Fukushima.

As a result, the climate scientist James Hanson and a colleague found that nuclear plants have actually saved nearly two million lives to date that would have been lost to air pollution.

Thanks to its energy density, nuclear plants require far less land than renewables. Even in sunny California, a solar farm requires 450 times more land to produce the same amount of energy as a nuclear plant.

Energy-dense nuclear requires far less in the way of materials, and produces far less in the way of waste compared to energy-dilute solar and wind.

A single Coke can’s worth of uranium provides all of the energy that the most gluttonous American or Australian lifestyle requires. At the end of the process, the high-level radioactive waste that nuclear plants produce is the very same Coke can of (used) uranium fuel. The reason nuclear is the best energy from an environmental perspective is because it produces so little waste and none enters the environment as pollution.

All of the waste fuel from 45 years of the Swiss nuclear program can fit, in canisters, on a basketball court-like warehouse, where like all spent nuclear fuel, it has never hurt a fly.

By contrast, solar panels require 17 times more materials in the form of cement, glass, concrete, and steel than do nuclear plants, and create over 200 times more waste.

We tend to think of solar panels as clean, but the truth is that there is no plan anywhere to deal with solar panels at the end of their 20 to 25 year lifespan.

Experts fear solar panels will be shipped, along with other forms of electronic waste, to be disassembled—or, more often, smashed with hammers—by poor communities in Africa and Asia, whose residents will be exposed to the dust from toxic heavy metals including lead, cadmium, and chromium.

Wherever I travel in the world I ask ordinary people what they think about nuclear and renewable energies. After saying they know next to nothing, they admit that nuclear is strong and renewables are weak. Their intuitions are correct. What most of us get wrong—understandably—is that weak energies are safer.

But aren’t renewables safer? The answer is no. Wind turbines, surprisingly, kill more people than nuclear plants.

In other words, the energy density of the fuel determines its environmental and health impacts. Spreading more mines and more equipment over larger areas of land is going to have larger environmental and human safety impacts.

It’s true that you can stand next to a solar panel without much harm while if you stand next to a nuclear reactor at full power you’ll die.

But when it comes to generating power for billions of people, it turns out that producing solar and wind collectors, and spreading them over large areas, has vastly worse impacts on humans and wildlife alike.

Our intuitive sense that sunlight is dilute sometimes shows up in films. That’s why nobody was shocked when the recent sequel of the dystopian sci-fi flick, “Blade Runner,” opened with a dystopian scene of California’s deserts paved with solar farms identical to the one that decimated desert tortoises.

Over the last several hundred years, human beings have been moving away from matter-dense fuels towards energy-dense ones. First we move from renewable fuels like wood, dung, and windmills, and towards the fossil fuels of coal, oil, and natural gas, and eventually to uranium.

Energy progress is overwhelmingly positive for people and nature. As we stop using wood for fuel we allow grasslands and forests to grow back, and the wildlife to return.

As we stop burning wood and dung in our homes, we no longer must breathe toxic indoor smoke. And as we move from fossil fuels to uranium we clear the outdoor air of pollution, and reduce how much we’ll heat up the planet.

Nuclear plants are thus a revolutionary technology—a grand historical break from fossil fuels as significant as the industrial transition from wood to fossil fuels before it.

The problem with nuclear is that it is unpopular, a victim of a 50 year-long concerted effort by fossil fuel, renewable energy, anti-nuclear weapons campaigners, and misanthropic environmentalists to ban the technology.

In response, the nuclear industry suffers battered wife syndrome, and constantly apologizes for its best attributes, from its waste to its safety.

Lately, the nuclear industry has promoted the idea that, in order to deal with climate change, “we need a mix of clean energy sources,” including solar, wind and nuclear. It was something I used to believe, and say, in part because it’s what people want to hear. The problem is that it’s not true.

France shows that moving from mostly nuclear electricity to a mix of nuclear and renewables results in more carbon emissions, due to using more natural gas, and higher prices, to the unreliability of solar and wind.

Oil and gas investors know this, which is why they made a political alliance with renewables companies, and why oil and gas companies have been spending millions of dollars on advertisements promoting solar, and funneling millions of dollars to said environmental groups to provide public relations cover.

What is to be done? The most important thing is for scientists and conservationists to start telling the truth about renewables and nuclear, and the relationship between energy density and environmental impact.

Bat scientists recently warned that wind turbines are on the verge of making one species, the Hoary bat, a migratory bat species, go extinct.

Another scientist who worked to build that gigantic solar farm in the California desert told High Country News, “Everybody knows that translocation of desert tortoises doesn’t work. When you’re walking in front of a bulldozer, crying, and moving animals, and cacti out of the way, it’s hard to think that the project is a good idea.”

I think it’s natural that those of us who became active on climate change gravitated toward renewables. They seemed like a way to harmonize human society with the natural world. Collectively, we have been suffering from an appeal-to-nature fallacy no different from the one that leads us to buy products at the supermarket labeled “all natural.” But it’s high time that those of us who appointed ourselves Earth’s guardians should take a second look at the science, and start questioning the impacts of our actions.

Now that we know that renewables can’t save the planet, are we really going to stand by and let them destroy it?

Weather forecasting

From Wikipedia, the free encyclopedia

Forecast of surface pressures five days into the future for the North Pacific, North America, and the North Atlantic Ocean

 

Weather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere at a given place and using meteorology to project how the atmosphere will change. 

Once calculated by hand based mainly upon changes in barometric pressure, current weather conditions, and sky condition or cloud cover, weather forecasting now relies on computer-based models that take many atmospheric factors into account. Human input is still required to pick the best possible forecast model to base the forecast upon, which involves pattern recognition skills, teleconnections, knowledge of model performance, and knowledge of model biases. The inaccuracy of forecasting is due to the chaotic nature of the atmosphere, the massive computational power required to solve the equations that describe the atmosphere, the error involved in measuring the initial conditions, and an incomplete understanding of atmospheric processes. Hence, forecasts become less accurate as the difference between current time and the time for which the forecast is being made (the range of the forecast) increases. The use of ensembles and model consensus help narrow the error and pick the most likely outcome. 

There are a variety of end uses to weather forecasts. Weather warnings are important forecasts because they are used to protect life and property. Forecasts based on temperature and precipitation are important to agriculture, and therefore to traders within commodity markets. Temperature forecasts are used by utility companies to estimate demand over coming days. On an everyday basis, people use weather forecasts to determine what to wear on a given day. Since outdoor activities are severely curtailed by heavy rain, snow and wind chill, forecasts can be used to plan activities around these events, and to plan ahead and survive them. In 2009, the US spent $5.1 billion on weather forecasting.

History

Ancient forecasting

For millennia people have tried to forecast the weather. In 650 BC, the Babylonians predicted the weather from cloud patterns as well as astrology. In about 350 BC, Aristotle described weather patterns in Meteorologica. Later, Theophrastus compiled a book on weather forecasting, called the Book of Signs. Chinese weather prediction lore extends at least as far back as 300 BC, which was also around the same time ancient Indian astronomers developed weather-prediction methods. In New Testament times, Christ himself referred to deciphering and understanding local weather patterns, by saying, "When evening comes, you say, 'It will be fair weather, for the sky is red', and in the morning, 'Today it will be stormy, for the sky is red and overcast.' You know how to interpret the appearance of the sky, but you cannot interpret the signs of the times."

In 904 AD, Ibn Wahshiyya's Nabatean Agriculture, translated into Arabic from an earlier Aramaic work, discussed the weather forecasting of atmospheric changes and signs from the planetary astral alterations; signs of rain based on observation of the lunar phases; and weather forecasts based on the movement of winds.

Ancient weather forecasting methods usually relied on observed patterns of events, also termed pattern recognition. For example, it might be observed that if the sunset was particularly red, the following day often brought fair weather. This experience accumulated over the generations to produce weather lore. However, not all of these predictions prove reliable, and many of them have since been found not to stand up to rigorous statistical testing.

Modern methods

The Royal Charter sank in an 1859 storm, stimulating the establishment of modern weather forecasting.

 

It was not until the invention of the electric telegraph in 1835 that the modern age of weather forecasting began. Before that, the fastest that distant weather reports could travel was around 100 miles per day (160 km/d), but was more typically 40–75 miles per day (60–120 km/day) (whether by land or by sea). By the late 1840s, the telegraph allowed reports of weather conditions from a wide area to be received almost instantaneously, allowing forecasts to be made from knowledge of weather conditions further upwind. 

The two men credited with the birth of forecasting as a science were an officer of the Royal Navy Francis Beaufort and his protégé Robert FitzRoy. Both were influential men in British naval and governmental circles, and though ridiculed in the press at the time, their work gained scientific credence, was accepted by the Royal Navy, and formed the basis for all of today's weather forecasting knowledge.

Beaufort developed the Wind Force Scale and Weather Notation coding, which he was to use in his journals for the remainder of his life. He also promoted the development of reliable tide tables around British shores, and with his friend William Whewell, expanded weather record-keeping at 200 British Coast guard stations. 

Robert FitzRoy was appointed in 1854 as chief of a new department within the Board of Trade to deal with the collection of weather data at sea as a service to mariners. This was the forerunner of the modern Meteorological Office. All ship captains were tasked with collating data on the weather and computing it, with the use of tested instruments that were loaned for this purpose.

Weather map of Europe, December 10, 1887.

 

A storm in 1859 that caused the loss of the Royal Charter inspired FitzRoy to develop charts to allow predictions to be made, which he called "forecasting the weather", thus coining the term "weather forecast". Fifteen land stations were established to use the telegraph to transmit to him daily reports of weather at set times leading to the first gale warning service. His warning service for shipping was initiated in February 1861, with the use of telegraph communications. The first daily weather forecasts were published in The Times in 1861. In the following year a system was introduced of hoisting storm warning cones at the principal ports when a gale was expected. The "Weather Book" which FitzRoy published in 1863 was far in advance of the scientific opinion of the time. 

As the electric telegraph network expanded, allowing for the more rapid dissemination of warnings, a national observational network was developed, which could then be used to provide synoptic analyses. Instruments to continuously record variations in meteorological parameters using photography were supplied to the observing stations from Kew Observatory – these cameras had been invented by Francis Ronalds in 1845 and his barograph had earlier been used by FitzRoy.

To convey accurate information, it soon became necessary to have a standard vocabulary describing clouds; this was achieved by means of a series of classifications first achieved by Luke Howard in 1802, and standardized in the International Cloud Atlas of 1896.

Numerical prediction

It was not until the 20th century that advances in the understanding of atmospheric physics led to the foundation of modern numerical weather prediction. In 1922, English scientist Lewis Fry Richardson published "Weather Prediction By Numerical Process", after finding notes and derivations he worked on as an ambulance driver in World War I. He described therein how small terms in the prognostic fluid dynamics equations governing atmospheric flow could be neglected, and a finite differencing scheme in time and space could be devised, to allow numerical prediction solutions to be found.

Richardson envisioned a large auditorium of thousands of people performing the calculations and passing them to others. However, the sheer number of calculations required was too large to be completed without the use of computers, and the size of the grid and time steps led to unrealistic results in deepening systems. It was later found, through numerical analysis, that this was due to numerical instability. The first computerised weather forecast was performed by a team composed of American meteorologists Jule Charney, Philip Thompson, Larry Gates, and Norwegian meteorologist Ragnar Fjørtoft, applied mathematician John von Neumann, and ENIAC programmer Klara Dan von Neumann. Practical use of numerical weather prediction began in 1955, spurred by the development of programmable electronic computers.

Broadcasts

The first ever daily weather forecasts were published in The Times on August 1, 1861, and the first weather maps were produced later in the same year. In 1911, the Met Office began issuing the first marine weather forecasts via radio transmission. These included gale and storm warnings for areas around Great Britain. In the United States, the first public radio forecasts were made in 1925 by Edward B. "E.B." Rideout, on WEEI, the Edison Electric Illuminating station in Boston. Rideout came from the U.S. Weather Bureau, as did WBZ weather forecaster G. Harold Noyes in 1931.

The world's first televised weather forecasts, including the use of weather maps, were experimentally broadcast by the BBC in 1936. This was brought into practice in 1949 after World War II. George Cowling gave the first weather forecast while being televised in front of the map in 1954. In America, experimental television forecasts were made by James C Fidler in Cincinnati in either 1940 or 1947 on the DuMont Television Network. In the late 1970s and early 80s, John Coleman, the first weatherman on ABC-TV's Good Morning America, pioneered the use of on-screen weather satellite information and computer graphics for television forecasts. Coleman was a co-founder of The Weather Channel (TWC) in 1982. TWC is now a 24-hour cable network. Some weather channels have started broadcasting on live broadcasting programs such as YouTube and Periscope to reach more viewers.

How models create forecasts

An example of 500 mbar geopotential height and absolute vorticity prediction from a numerical weather prediction model

The basic idea of numerical weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future. The main inputs from country-based weather services are surface observations from automated weather stations at ground level over land and from weather buoys at sea. The World Meteorological Organization acts to standardize the instrumentation, observing practices and timing of these observations worldwide. Stations either report hourly in METAR reports, or every six hours in SYNOP reports. Sites launch radiosondes, which rise through the depth of the troposphere and well into the stratosphere. Data from weather satellites are used in areas where traditional data sources are not available. Compared with similar data from radiosondes, the satellite data has the advantage of global coverage, however at a lower accuracy and resolution. Meteorological radar provide information on precipitation location and intensity, which can be used to estimate precipitation accumulations over time. Additionally, if a pulse Doppler weather radar is used then wind speed and direction can be determined.

Modern weather predictions aid in timely evacuations and potentially save lives and prevent property damage

Commerce provides pilot reports along aircraft routes, and ship reports along shipping routes. Research flights using reconnaissance aircraft fly in and around weather systems of interest such as tropical cyclones. Reconnaissance aircraft are also flown over the open oceans during the cold season into systems that cause significant uncertainty in forecast guidance, or are expected to be of high impact 3–7 days into the future over the downstream continent.

Models are initialized using this observed data. The irregularly spaced observations are processed by data assimilation and objective analysis methods, which perform quality control and obtain values at locations usable by the model's mathematical algorithms (usually an evenly spaced grid). The data are then used in the model as the starting point for a forecast. Commonly, the set of equations used to predict the known as the physics and dynamics of the atmosphere are called primitive equations. These equations are initialized from the analysis data and rates of change are determined. The rates of change predict the state of the atmosphere a short time into the future. The equations are then applied to this new atmospheric state to find new rates of change, and these new rates of change predict the atmosphere at a yet further time into the future. This time stepping procedure is continually repeated until the solution reaches the desired forecast time.

The length of the time step chosen within the model is related to the distance between the points on the computational grid, and is chosen to maintain numerical stability. Time steps for global models are on the order of tens of minutes, while time steps for regional models are between one and four minutes. The global models are run at varying times into the future. The Met Office's Unified Model is run six days into the future, the European Centre for Medium-Range Weather Forecasts model is run out to 10 days into the future, while the Global Forecast System model run by the Environmental Modeling Center is run 16 days into the future. The visual output produced by a model solution is known as a prognostic chart, or prog. The raw output is often modified before being presented as the forecast. This can be in the form of statistical techniques to remove known biases in the model, or of adjustment to take into account consensus among other numerical weather forecasts. MOS or model output statistics is a technique used to interpret numerical model output and produce site-specific guidance. This guidance is presented in coded numerical form, and can be obtained for nearly all National Weather Service reporting stations in the United States. As proposed by Edward Lorenz in 1963, long range forecasts, those made at a range of two weeks or more, are impossible to definitively predict the state of the atmosphere, owing to the chaotic nature of the fluid dynamics equations involved. In numerical models, extremely small errors in initial values double roughly every five days for variables such as temperature and wind velocity.

Essentially, a model is a computer program that produces meteorological information for future times at given locations and altitudes. Within any modern model is a set of equations, known as the primitive equations, used to predict the future state of the atmosphere. These equations—along with the ideal gas law—are used to evolve the density, pressure, and potential temperature scalar fields and the velocity vector field of the atmosphere through time. Additional transport equations for pollutants and other aerosols are included in some primitive-equation mesoscale models as well. The equations used are nonlinear partial differential equations, which are impossible to solve exactly through analytical methods, with the exception of a few idealized cases. Therefore, numerical methods obtain approximate solutions. Different models use different solution methods: some global models use spectral methods for the horizontal dimensions and finite difference methods for the vertical dimension, while regional models and other global models usually use finite-difference methods in all three dimensions.

Techniques

Persistence

The simplest method of forecasting the weather, persistence, relies upon today's conditions to forecast the conditions tomorrow. This can be a valid way of forecasting the weather when it is in a steady state, such as during the summer season in the tropics. This method of forecasting strongly depends upon the presence of a stagnant weather pattern. Therefore, when in a fluctuating weather pattern, this method of forecasting becomes inaccurate. It can be useful in both short range forecasts and long range forecasts.

Use of a barometer

Measurements of barometric pressure and the pressure tendency (the change of pressure over time) have been used in forecasting since the late 19th century. The larger the change in pressure, especially if more than 3.5 hPa (2.6 mmHg), the larger the change in weather can be expected. If the pressure drop is rapid, a low pressure system is approaching, and there is a greater chance of rain. Rapid pressure rises are associated with improving weather conditions, such as clearing skies.

Looking at the sky

Marestail shows moisture at high altitude, signalling the later arrival of wet weather.

 

Along with pressure tendency, the condition of the sky is one of the more important parameters used to forecast weather in mountainous areas. Thickening of cloud cover or the invasion of a higher cloud deck is indicative of rain in the near future. High thin cirrostratus clouds can create halos around the sun or moon, which indicates an approach of a warm front and its associated rain. Morning fog portends fair conditions, as rainy conditions are preceded by wind or clouds that prevent fog formation. The approach of a line of thunderstorms could indicate the approach of a cold front. Cloud-free skies are indicative of fair weather for the near future. A bar can indicate a coming tropical cyclone. The use of sky cover in weather prediction has led to various weather lore over the centuries.

Nowcasting

The forecasting of the weather within the next six hours is often referred to as nowcasting. In this time range it is possible to forecast smaller features such as individual showers and thunderstorms with reasonable accuracy, as well as other features too small to be resolved by a computer model. A human given the latest radar, satellite and observational data will be able to make a better analysis of the small scale features present and so will be able to make a more accurate forecast for the following few hours. However, there are now expert systems using those data and mesoscale numerical model to make better extrapolation, including evolution of those features in time.

Use of forecast models

An example of 500 mbar geopotential height prediction from a numerical weather prediction model

 

In the past, the human forecaster was responsible for generating the entire weather forecast based upon available observations. Today, human input is generally confined to choosing a model based on various parameters, such as model biases and performance. Using a consensus of forecast models, as well as ensemble members of the various models, can help reduce forecast error. However, regardless how small the average error becomes with any individual system, large errors within any particular piece of guidance are still possible on any given model run. Humans are required to interpret the model data into weather forecasts that are understandable to the end user. Humans can use knowledge of local effects that may be too small in size to be resolved by the model to add information to the forecast. While increasing accuracy of forecast models implies that humans may no longer be needed in the forecast process at some point in the future, there is currently still a need for human intervention.

Analog technique

The analog technique is a complex way of making a forecast, requiring the forecaster to remember a previous weather event that is expected to be mimicked by an upcoming event. What makes it a difficult technique to use is that there is rarely a perfect analog for an event in the future. Some call this type of forecasting pattern recognition. It remains a useful method of observing rainfall over data voids such as oceans, as well as the forecasting of precipitation amounts and distribution in the future. A similar technique is used in medium range forecasting, which is known as teleconnections, when systems in other locations are used to help pin down the location of another system within the surrounding regime. An example of teleconnections are by using El Niño-Southern Oscillation (ENSO) related phenomena.

Communicating forecasts to the public

An example of a two-day weather forecast in the visual style that an American newspaper might use. Temperatures are given in Fahrenheit.

 

Most end users of forecasts are members of the general public. Thunderstorms can create strong winds and dangerous lightning strikes that can lead to deaths, power outages, and widespread hail damage. Heavy snow or rain can bring transportation and commerce to a stand-still, as well as cause flooding in low-lying areas. Excessive heat or cold waves can sicken or kill those with inadequate utilities, and droughts can impact water usage and destroy vegetation. 

Several countries employ government agencies to provide forecasts and watches/warnings/advisories to the public in order to protect life and property and maintain commercial interests. Knowledge of what the end user needs from a weather forecast must be taken into account to present the information in a useful and understandable way. Examples include the National Oceanic and Atmospheric Administration's National Weather Service (NWS) and Environment Canada's Meteorological Service (MSC). Traditionally, newspaper, television, and radio have been the primary outlets for presenting weather forecast information to the public. In addition, some cities had weather beacons. Increasingly, the internet is being used due to the vast amount of specific information that can be found. In all cases, these outlets update their forecasts on a regular basis.

Severe weather alerts and advisories

A major part of modern weather forecasting is the severe weather alerts and advisories that the national weather services issue in the case that severe or hazardous weather is expected. This is done to protect life and property. Some of the most commonly known of severe weather advisories are the severe thunderstorm and tornado warning, as well as the severe thunderstorm and tornado watch. Other forms of these advisories include winter weather, high wind, flood, tropical cyclone, and fog. Severe weather advisories and alerts are broadcast through the media, including radio, using emergency systems as the Emergency Alert System, which break into regular programming.

Low temperature forecast

The low temperature forecast for the current day is calculated using the lowest temperature found between 7 pm that evening through 7 am the following morning. So, in short, today's forecasted low is most likely tomorrow's low temperature.

Specialist forecasting

There are a number of sectors with their own specific needs for weather forecasts and specialist services are provided to these users.

Air traffic

Ash cloud from the 2008 eruption of Chaitén volcano stretching across Patagonia from the Pacific to the Atlantic Ocean

 

Because the aviation industry is especially sensitive to the weather, accurate weather forecasting is essential. Fog or exceptionally low ceilings can prevent many aircraft from landing and taking off. Turbulence and icing are also significant in-flight hazards. Thunderstorms are a problem for all aircraft because of severe turbulence due to their updrafts and outflow boundaries, icing due to the heavy precipitation, as well as large hail, strong winds, and lightning, all of which can cause severe damage to an aircraft in flight. Volcanic ash is also a significant problem for aviation, as aircraft can lose engine power within ash clouds. On a day-to-day basis airliners are routed to take advantage of the jet stream tailwind to improve fuel efficiency. Aircrews are briefed prior to takeoff on the conditions to expect en route and at their destination. Additionally, airports often change which runway is being used to take advantage of a headwind. This reduces the distance required for takeoff, and eliminates potential crosswinds.

Marine

Commercial and recreational use of waterways can be limited significantly by wind direction and speed, wave periodicity and heights, tides, and precipitation. These factors can each influence the safety of marine transit. Consequently, a variety of codes have been established to efficiently transmit detailed marine weather forecasts to vessel pilots via radio, for example the MAFOR (marine forecast). Typical weather forecasts can be received at sea through the use of RTTY, Navtex and Radiofax.

Agriculture

Farmers rely on weather forecasts to decide what work to do on any particular day. For example, drying hay is only feasible in dry weather. Prolonged periods of dryness can ruin cotton, wheat, and corn crops. While corn crops can be ruined by drought, their dried remains can be used as a cattle feed substitute in the form of silage. Frosts and freezes play havoc with crops both during the spring and fall. For example, peach trees in full bloom can have their potential peach crop decimated by a spring freeze. Orange groves can suffer significant damage during frosts and freezes, regardless of their timing.

Forestry

Weather forecasting of wind, precipitations and humidity is essential for preventing and controlling wildfires. Different indices, like the Forest fire weather index and the Haines Index, have been developed to predict the areas more at risk to experience fire from natural or human causes. Conditions for the development of harmful insects can be predicted by forecasting the evolution of weather, too.

Utility companies

An air handling unit is used for the heating and cooling of air in a central location (click on image for legend).

 

Electricity and gas companies rely on weather forecasts to anticipate demand, which can be strongly affected by the weather. They use the quantity termed the degree day to determine how strong of a use there will be for heating (heating degree day) or cooling (cooling degree day). These quantities are based on a daily average temperature of 65 °F (18 °C). Cooler temperatures force heating degree days (one per degree Fahrenheit), while warmer temperatures force cooling degree days. In winter, severe cold weather can cause a surge in demand as people turn up their heating. Similarly, in summer a surge in demand can be linked with the increased use of air conditioning systems in hot weather. By anticipating a surge in demand, utility companies can purchase additional supplies of power or natural gas before the price increases, or in some circumstances, supplies are restricted through the use of brownouts and blackouts.

Other commercial companies

Increasingly, private companies pay for weather forecasts tailored to their needs so that they can increase their profits or avoid large losses. For example, supermarket chains may change the stocks on their shelves in anticipation of different consumer spending habits in different weather conditions. Weather forecasts can be used to invest in the commodity market, such as futures in oranges, corn, soybeans, and oil.

Military applications

United Kingdom Armed Forces

Royal Navy

The UK Royal Navy, working with the UK Met Office, has its own specialist branch of weather observers and forecasters, as part of the Hydrographic and Meteorological (HM) specialisation, who monitor and forecast operational conditions across the globe, to provide accurate and timely weather and oceanographic information to submarines, ships and Fleet Air Arm aircraft.

Royal Air Force

A mobile unit in the RAF, working with the UK Met Office, forecasts the weather for regions in which British, allied servicemen and women are deployed. A group based at Camp Bastion provides forecasts for the British armed forces in Afghanistan.

United States Armed Forces

US Navy

Emblem of JTWC Joint Typhoon Warning Center

 

Similar to the private sector, military weather forecasters present weather conditions to the war fighter community. Military weather forecasters provide pre-flight and in-flight weather briefs to pilots and provide real time resource protection services for military installations. Naval forecasters cover the waters and ship weather forecasts. The United States Navy provides a special service to both themselves and the rest of the federal government by issuing forecasts for tropical cyclones across the Pacific and Indian Oceans through their Joint Typhoon Warning Center.

US Air Force

Within the United States, Air Force Weather provides weather forecasting for the Air Force and the Army. Air Force forecasters cover air operations in both wartime and peacetime operations and provide Army support; United States Coast Guard marine science technicians provide ship forecasts for ice breakers and other various operations within their realm; and Marine forecasters provide support for ground- and air-based United States Marine Corps operations. All four military branches take their initial enlisted meteorology technical training at Keesler Air Force Base. Military and civilian forecasters actively cooperate in analyzing, creating and critiquing weather forecast products.