Day: February 10, 2023

As rain clouds swelled over Fort Stockton, Texas, last summer, a little yellow plane zipped through the sky. It was on a mission.

Equipped with tanks of water and special nozzles on its wings, the craft soared beneath the gray-white billows. Then, at just the right moment, it released a spray of electrically charged water particles into the cloud.

The goal? To squeeze some extra rain from the West Texas sky.

“Water’s becoming very valuable and more and more scarce,” said Dan Martin, a research engineer with Department of Agriculture’s Agricultural Research Service who helped invent the technology.

It’s a new spin on a decades-old practice known as “cloud seeding,” or efforts to boost precipitation by spraying special particles into the air. It’s one of the world’s most popular forms of weather modification, and it’s practiced across much of the western U.S., as well as China, Russia, parts of the Middle East and other countries.

Developing new and improved forms of cloud seeding has taken on a greater urgency in recent years. Severe drought around the world, worsened by the steady progression of climate change, has sparked a growing interest in innovative forms of water management from researchers, governments and corporate giants.

Cloud seeding can be relatively cheap compared with other water management strategies, like desalination, a chemical process that removes salts and other minerals from water to make it safe for drinking.

But there’s a catch. It’s notoriously difficult to design experiments that demonstrate how well the technology actually works (Climatewire, March 16, 2021).

Even as researchers work to develop more effective forms of cloud seeding, scientists say it’s hard to tell for sure if it makes a difference.

“There’s no question that cloud seeding works — but the question is how much do we really produce?” said Katja Friedrich, an atmospheric scientist and cloud seeding expert at the University of Colorado, Boulder.

Cloud seeding hasn’t changed much since the technology was first demonstrated in the 1940s.

Clouds form as water droplets condense in the sky. Certain types of particles are good at attracting water or ice and, in theory, can help speed the process along. Silver iodide and other kinds of salt particles have been commonly used in cloud seeding for decades.

But the idea of using charged water particles is relatively new.

It’s rooted in a simple theory. The bottoms of rain clouds are naturally filled with negatively charged water. Hit the cloud with a stream of positively charged particles and the water droplets will “collide and coalesce,” Martin said.

“Do that enough times and it creates precipitable rain,” he added.

Last summer’s flights over Texas were the latest tests in a USDA research project that’s been ongoing for several years. Meanwhile, research groups around the world are working on similar projects aimed at juicing the effectiveness of cloud seeding operations.

Some, like Martin’s, are using electrical charges.

Researchers at Britain’s University of Reading and University of Bath used drones to zap clouds with electrical pulses. The project, which began in 2017, was bankrolled by the United Arab Emirates and concluded last year.

Another UAE-funded project is experimenting with nanotechnology, by seeding clouds with special nanoengineered particles. The emirates is funding a separate effort that uses artificial intelligence to build algorithms that can more accurately predict the kinds of weather conditions best suited for cloud seeding.

Rain for oil?

A Malaysian military plane conducts a cloud seeding operation in an attempt to clear haze from plantation fires by shooting water and salt into the sky in 2019. | AP Photo/Vincent Thian

More than a dozen firms, research institutions or individuals have patented at least 19 cloud seeding technologies or methods since 2018, according to an E&E News review of international patents. The “aerial electrostatic system for weather modification” that Martin invented and is now testing is included in that tally.

Several companies have also taken an interest.

Last March, the Saudi Arabian Oil Co. — the world’s third-most valuable publicly traded firm — obtained a U.S. patent for generating rain “to support water flooding in remote oil fields.” Drillers need water to test wells and increase oil production. But that resource can be hard to come by in the desert environments where the company, also known as Saudi Aramco, mainly operates.

The process Saudi Aramco patented would seed clouds using silver iodide or other materials and then collect the rainfall in reservoirs it could draw on to boost oil production. It’s unclear if the oil giant has deployed the process. Saudi Aramco declined to comment for this story.

Weather modification startup WeatherTec AG is another example. Based in Zug, Switzerland, with offices in Germany and Jordan, the company uses giant umbrella-shaped devices to charge humidity and clouds with what it says are rain-producing ions.

WeatherTec’s patents — obtained from the European Patent Office and the World Intellectual Property Organization — appear to be for new devices that it isn’t yet marketing to potential customers. The company didn’t respond to requests for comment.

In 2019, U.S. aircraft maker Boeing Co. received a U.S. patent on “a system for use in inducing rainfall.” A Boeing spokesperson declined to elaborate on how the company is using the system, if at all.

Much of the recent explosion in new cloud seeding research has originated in the UAE, according to Friedrich, the University of Colorado scientist.

The country has experimented with cloud seeding for decades, and its Research Program for Rain Enhancement Science (UAEREP) has awarded grants for at least 11 different research projects involving weather modification since 2015. Awarded projects receive up to $1.5 million in funding distributed over three years.

Cloud-seeding research has historically been dominated by commercial companies rather than independent scientists, Keri Nicoll, a University of Reading researcher working on the UAE-funded drone project, said in an email. That’s begun to change. Recent funding initiatives like UAEREP “have really driven research in this area forward in the last 5-6 years,” she said.

Temperatures are rising faster than the global average across much of the Middle East, and precipitation is declining. Studies suggest that droughts will grow increasingly severe as the region continues to warm.

“They are heavily investing into cloud seeding because of obvious reasons,” Friedrich said. “They need the water.”

China, which has also recently struggled with record-breaking drought, is emerging as another front-runner in altering the weather. In 2020, the country announced plans to rapidly expand its national weather modification program to encompass an area covering more than 2 million square miles.

‘Not the Holy Grail’

Interest in new cloud-seeding technology is growing in the western United States, as well. Friedrich attributes that in part to a groundbreaking study she co-authored in 2020. It was one of the first research papers to quantitatively demonstrate that cloud seeding works.

To show a real effect, scientists must prove that the rainfall from a seeded cloud wouldn’t have happened without the seeding. That requires two sets of experiments using identical types of clouds in the same location under the same conditions — one with seeding and one without.

That’s difficult to accomplish in the real world, where weather conditions are constantly changing. For decades, scientists have relied mainly on statistical studies instead. Typically, that involves seeding a cloud in one location while monitoring unseeded clouds in nearby locations and comparing the results. Those findings are less scientifically convincing — but they’re a start.

Statistical studies have suggested that cloud-seeding operations may boost rainfall by as much as 15 or 20 percent.

But Friedrich’s project, which took place in Idaho’s Payette River Basin in 2017, managed to luck into a nearly perfect experiment. Local weather conditions allowed it to compare the effects of seeding clouds in the same location for three days straight. In that time, scientists estimated that the seeded clouds produced about 286 Olympic swimming pools’ worth of snow.

The project effectively proved that cloud seeding works. But how well it works is another question. It doesn’t prove that the same amount of rain or snow would fall in different places under different conditions.

Scientists can use data from experiments like Friedrich’s to build models that simulate cloud seeding operations, helping answer those questions. But in the absence of such data, many research projects still rely on statistical studies.

It’s not a perfect solution. But some limited data appears to be promising. Trials of Martin’s charged-water technology, for instance, suggest that it might be twice as effective as conventional cloud seeding efforts.

Still, even if cloud seeding can marginally increase Western water supplies, it has its limits. For one thing, it requires clouds, making it less useful during droughts.

That makes it a strategy that requires lots of advance planning, Martin said. It should be used to shore up water supplies before drought strikes.

“Most people don’t think about the need for cloud seeding when times are good — when we have ample rainfall,” he said. “It’s when we have times of drought that they think about it, but by then it’s too late.”

And since there’s still great uncertainty about how well even conventional cloud-seeding technologies work, Friedrich cautions that “you don’t want to put all your eggs into this one basket.”

Cloud seeding could prove useful as one tool in the arsenal — but water managers should have other strategies in hand.

“If I were a water manager, I would consider it,” Friedrich said. “But this is not the Holy Grail or what really solves all the problems.”

Reprinted from E&E News with permission from POLITICO, LLC. Copyright 2023. E&E News provides essential news for energy and environment professionals.

Empowered by artificial intelligence technologies, computers today can engage in convincing conversations with people, compose songs, paint paintings, play chess and go, and diagnose diseases, to name just a few examples of their technological prowess.

These successes could be taken to indicate that computation has no limits. To see if that’s the case, it’s important to understand what makes a computer powerful.

There are two aspects to a computer’s power: the number of operations its hardware can execute per second and the efficiency of the algorithms it runs. The hardware speed is limited by the laws of physics. Algorithms – basically sets of instructions – are written by humans and translated into a sequence of operations that computer hardware can execute. Even if a computer’s speed could reach the physical limit, computational hurdles remain due to the limits of algorithms.

These hurdles include problems that are impossible for computers to solve and problems that are theoretically solvable but in practice are beyond the capabilities of even the most powerful versions of today’s computers imaginable. Mathematicians and computer scientists attempt to determine whether a problem is solvable by trying them out on an imaginary machine.

An imaginary computing machine

The modern notion of an algorithm, known as a Turing machine, was formulated in 1936 by British mathematician Alan Turing. It’s an imaginary device that imitates how arithmetic calculations are carried out with a pencil on paper. The Turing machine is the template all computers today are based on.

To accommodate computations that would need more paper if done manually, the supply of imaginary paper in a Turing machine is assumed to be unlimited. This is equivalent to an imaginary limitless ribbon, or “tape,” of squares, each of which is either blank or contains one symbol.

The machine is controlled by a finite set of rules and starts on an initial sequence of symbols on the tape. The operations the machine can carry out are moving to a neighboring square, erasing a symbol and writing a symbol on a blank square. The machine computes by carrying out a sequence of these operations. When the machine finishes, or “halts,” the symbols remaining on the tape are the output or result.

What is a Turing machine?

Computing is often about decisions with yes or no answers. By analogy, a medical test (type of problem) checks if a patient’s specimen (an instance of the problem) has a certain disease indicator (yes or no answer). The instance, represented in a Turing machine in digital form, is the initial sequence of symbols.

A problem is considered “solvable” if a Turing machine can be designed that halts for every instance whether positive or negative and correctly determines which answer the instance yields.

Not every problem can be solved

Many problems are solvable using a Turing machine and therefore can be solved on a computer, while many others are not. For example, the domino problem, a variation of the tiling problem formulated by Chinese American mathematician Hao Wang in 1961, is not solvable.

The task is to use a set of dominoes to cover an entire grid and, following the rules of most dominoes games, matching the number of pips on the ends of abutting dominoes. It turns out that there is no algorithm that can start with a set of dominoes and determine whether or not the set will completely cover the grid.

Keeping it reasonable

A number of solvable problems can be solved by algorithms that halt in a reasonable amount of time. These “polynomial-time algorithms” are efficient algorithms, meaning it’s practical to use computers to solve instances of them.

Thousands of other solvable problems are not known to have polynomial-time algorithms, despite ongoing intensive efforts to find such algorithms. These include the Traveling Salesman Problem.

The Traveling Salesman Problem asks whether a set of points with some points directly connected, called a graph, has a path that starts from any point and goes through every other point exactly once, and comes back to the original point. Imagine that a salesman wants to find a route that passes all households in a neighborhood exactly once and returns to the starting point.

The Traveling Salesman Problem quickly gets out of hand when you get beyond a few destinations.

These problems, called NP-complete, were independently formulated and shown to exist in the early 1970s by two computer scientists, American Canadian Stephen Cook and Ukrainian American Leonid Levin. Cook, whose work came first, was awarded the 1982 Turing Award, the highest in computer science, for this work.

The cost of knowing exactly

The best-known algorithms for NP-complete problems are essentially searching for a solution from all possible answers. The Traveling Salesman Problem on a graph of a few hundred points would take years to run on a supercomputer. Such algorithms are inefficient, meaning there are no mathematical shortcuts.

Practical algorithms that address these problems in the real world can only offer approximations, though the approximations are improving. Whether there are efficient polynomial-time algorithms that can solve NP-complete problems is among the seven millennium open problems posted by the Clay Mathematics Institute at the turn of the 21st century, each carrying a prize of US$1 million.

Beyond Turing

Could there be a new form of computation beyond Turing’s framework? In 1982, American physicist Richard Feynman, a Nobel laureate, put forward the idea of computation based on quantum mechanics.

What is a quantum computer?

In 1995, Peter Shor, an American applied mathematician, presented a quantum algorithm to factor integers in polynomial time. Mathematicians believe that this is unsolvable by polynomial-time algorithms in Turing’s framework. Factoring an integer means finding a smaller integer greater than 1 that can divide the integer. For example, the integer 688,826,081 is divisible by a smaller integer 25,253, because 688,826,081 = 25,253 x 27,277.

A major algorithm called the RSA algorithm, widely used in securing network communications, is based on the computational difficulty of factoring large integers. Shor’s result suggests that quantum computing, should it become a reality, will change the landscape of cybersecurity.

Can a full-fledged quantum computer be built to factor integers and solve other problems? Some scientists believe it can be. Several groups of scientists around the world are working to build one, and some have already built small-scale quantum computers.

Nevertheless, like all novel technologies invented before, issues with quantum computation are almost certain to arise that would impose new limits.

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Jie Wang is a professor of Computer Science at UMass Lowell.

This article is republished from The Conversation under a Creative Commons license. Read the original article.