Meteorologist Todd Dankers monitors weather patterns in Boulder, Colorado, Oct. 24, 2018. Hyoung Chang/The Denver Post via Getty Images
A century ago, English mathematician Lewis Fry Richardson proposed a startling idea for that time: constructing a systematic process based on math for predicting the weather. In his 1922 book, Weather Prediction By Numerical Process, Richardson tried to write an equation that he could use to solve the dynamics of the atmosphere based on hand calculations.
It didnt work because not enough was known about the science of the atmosphere at that time. Perhaps some day in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream, Richardson concluded.
People are also reading
A century later, modern weather forecasts are based on the kind of complex computations that Richardson imagined and theyve become more accurate than anything he envisioned. Especially in recent decades, steady progress in research, data and computing has enabled a quiet revolution of numerical weather prediction.
For example, a forecast of heavy rainfall two days in advance is now as good as a same-day forecast was in the mid-1990s. Errors in the predicted tracks of hurricanes have been cut in half in the last 30 years.
There still are major challenges. Thunderstorms that produce tornadoes, large hail or heavy rain remain difficult to predict. And then theres chaos, often described as the butterfly effect the fact that small changes in complex processes make weather less predictable. Chaos limits our ability to make precise forecasts beyond about 10 days.
As in many other scientific fields, the proliferation of tools like artificial intelligence and machine learning holds great promise for weather prediction. We have seen some of whats possible in our research on applying machine learning to forecasts of high-impact weather. But we also believe that while these tools open up new possibilities for better forecasts, many parts of the job are handled more skillfully by experienced people.
Australian meteorologist Dean Narramore explains why its hard to forecast large thunderstorms.
Predictions based on storm history
Today, weather forecasters primary tools are numerical weather prediction models. These models use observations of the current state of the atmosphere from sources such as weather stations, weather balloons and satellites, and solve equations that govern the motion of air.
These models are outstanding at predicting most weather systems, but the smaller a weather event is, the more difficult it is to predict. As an example, think of a thunderstorm that dumps heavy rain on one side of town and nothing on the other side. Furthermore, experienced forecasters are remarkably good at synthesizing the huge amounts of weather information they have to consider each day, but their memories and bandwidth are not infinite.
Artificial intelligence and machine learning can help with some of these challenges. Forecasters are using these tools in several ways now, including making predictions of high-impact weather that the models cant provide.
In a project that started in 2017 and was reported in a 2021 paper, we focused on heavy rainfall. Of course, part of the problem is defining heavy: Two inches of rain in New Orleans may mean something very different than in Phoenix. We accounted for this by using observations of unusually large rain accumulations for each location across the country, along with a history of forecasts from a numerical weather prediction model.
We plugged that information into a machine learning method known as random forests, which uses many decision trees to split a mass of data and predict the likelihood of different outcomes. The result is a tool that forecasts the probability that rains heavy enough to generate flash flooding will occur.
We have since applied similar methods to forecasting of tornadoes, large hail and severe thunderstorm winds. Other research groups are developing similar tools. National Weather Service forecasters are using some of these tools to better assess the likelihood of hazardous weather on a given day.
An excessive rainfall forecast from the Colorado State University-Machine Learning Probabilities system for the extreme rainfall associated with the remnants of Hurricane Ida in the mid-Atlantic states in September 2021. The left panel shows the forecast probability of excessive rainfall, available on the morning of Aug. 31, more than 24 hours ahead of the event. The right panel shows the resulting observations of excessive rainfall. The machine learning program correctly highlighted the corridor where widespread heavy rain and flooding would occur. Russ Schumacher and Aaron Hill, CC BY-ND
Researchers also are embedding machine learning within numerical weather prediction models to speed up tasks that can be intensive to compute, such as predicting how water vapor gets converted to rain, snow or hail.
Its possible that machine learning models could eventually replace traditional numerical weather prediction models altogether. Instead of solving a set of complex physical equations as the models do, these systems instead would process thousands of past weather maps to learn how weather systems tend to behave. Then, using current weather data, they would make weather predictions based on what theyve learned from the past.
Some studies have shown that machine learning-based forecast systems can predict general weather patterns as well as numerical weather prediction models while using only a fraction of the computing power the models require. These new tools dont yet forecast the details of local weather that people care about, but with many researchers carefully testing them and inventing new methods, there is promise for the future.
A forecast from the Colorado State University-Machine Learning Probabilities system for the severe weather outbreak on Dec. 15, 2021, in the U.S. Midwest. The panels illustrate the progression of the forecast from eight days in advance (lower right) to three days in advance (upper left), along with reports of severe weather (tornadoes in red, hail in green, damaging wind in blue). Russ Schumacher and Aaron Hill, CC BY-ND
The role of human expertise
There are also reasons for caution. Unlike numerical weather prediction models, forecast systems that use machine learning are not constrained by the physical laws that govern the atmosphere. So its possible that they could produce unrealistic results for example, forecasting temperature extremes beyond the bounds of nature. And it is unclear how they will perform during highly unusual or unprecedented weather phenomena.
And relying on AI tools can raise ethical concerns. For instance, locations with relatively few weather observations with which to train a machine learning system may not benefit from forecast improvements that are seen in other areas.
Another central question is how best to incorporate these new advances into forecasting. Finding the right balance between automated tools and the knowledge of expert human forecasters has long been a challenge in meteorology. Rapid technological advances will only make it more complicated.
Ideally, AI and machine learning will allow human forecasters to do their jobs more efficiently, spending less time on generating routine forecasts and more on communicating forecasts implications and impacts to the public or, for private forecasters, to their clients. We believe that careful collaboration between scientists, forecasters and forecast users is the best way to achieve these goals and build trust in machine-generated weather forecasts.
Russ Schumacher receives funding from the National Oceanic and Atmospheric Administration for research on applying machine learning to improve forecasts of high-impact weather.
Aaron Hill receives funding from the National Oceanic and Atmospheric Administration to research machine learning applications that improve high-impact weather forecasts.
This article is republished fromThe Conversationunder a Creative Commons license.
The fast winds, rapid rainfall, and huge storm surges of hurricanes make this natural disaster responsible for many deaths and millions of dollars worth of damage each year. Capable of triggering flash floods, mudslides, and tornadoes, even weak hurricanes can cause extensive destruction to property, infrastructure, and crops. Other hurricanes remain at sea and never make landfall, limiting the destruction they cause. Advancements in technology, particularly satellite imaging, have greatly improved warnings and advisories that prompted live-saving evacuations. But not all lives can be spared.
Also known as tropical cyclones, hurricanes are large, wet storms with high winds that form over warm water. Hurricane season in the Atlantic Basinthe Atlantic Ocean, Gulf of Mexico, and the Caribbean Searuns from June 1 to Nov. 30 each year, though some hurricanes do form outside of this season. Many tropical storms are produced on an average year, though not all reach the strength of hurricanes.
Hurricanes are rated using the Saffir-Simpson Hurricane Wind Scale. Category 1 hurricanes have the lowest wind speeds at 74-95 miles per hour, and Category 5 hurricanes have the strongest winds at 157 miles per hour or higher. Storms that are Category 3 and above are considered major hurricanes.
It seems hurricanes and other weather disasters are becoming increasingly destructive. There were 30 named storms and14 hurricanes during the 2020 Atlantic hurricane season, with seven of those 14 hurricanes considered major. According to the National Oceanic and Atmospheric Administration (NOAA), 2020 marked "the fifth consecutive above-normal Atlantic hurricane season." The NOAA predicted another above-average season for 2021, a forecast already coming true.
Some hurricane seasons are worse than others. In 1920, the strongest hurricane was a Category 2 storm that killed one person in Louisiana. Others are devastating and destroy entire cities. Hurricane Katrina, an infamous storm that struck the U.S. in 2005, delivered lasting damage to New Orleans and cost the country over $100 billion.
Stacker obtained hurricane data, updated in 2020, from the NOAA's Atlantic Oceanic and Meteorological Laboratory. A list of notable events or facts from each year was compiled from news, scientific, and government reports. Read on to learn about the noteworthy tropical storms and hurricanes from the year you were born.
You may also like:How to prepare for 15 types of emergencies
- Named storms: 5 (6.00 less than average)
- Hurricanes: 2 (3.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
Because there was no satellite imagery in 1919, meteorologists temporarily lost track of a Category 4 Atlantic Gulf hurricane when ships stopped transmitting information about it. This storm was the deadliest hurricane ever to hit the Texas Coastal Bend, and it caused more than 500 people to die or be lost due to sinking or missing ships.
[Pictured: Map plotting the track and the intensity of the 1919 hurricane, according to the SaffirSimpson scale.]
- Named storms: 5 (6.00 less than average)
- Hurricanes: 4 (1.91 less than average)
- Category 3 or higher hurricanes: 0 (2.52 less than average)
The 1920 hurricane season was less active than usual. One of the year's most notable storms was a Category 2 hurricane that hit Louisiana, killing one person. The storm ruined the sugar crop and caused $1.45 million in total damages.
- Named storms: 7 (4.00 less than average)
- Hurricanes: 5 (0.91 less than average)
- Category 3 or higher hurricanes: 2 (0.52 less than average)
On Oct. 28, 1921, Tampa Bay, Florida, experienced its most damaging hurricane since 1848. The unnamed hurricane killed eight people and cost over $5 million, not adjusted for inflation. It smashed boats against docks and destroyed parts of the local sea wall.
[Pictured: Wreckage of Safety Harbor Springs Pavillion after the 1921 hurricane.]
- Named storms: 5 (6.00 less than average)
- Hurricanes: 3 (2.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
No hurricanes made landfall in the U.S. during the 1922 hurricane season. However, a hurricane that downgraded to a tropical storm did strike El Salvador, overflowing the Rio Grande and causing more than $5 million of damage.
- Named storms: 9 (2.00 less than average)
- Hurricanes: 4 (1.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
The 1923 hurricane season featured the most tropical storms since 1916. This count includes four hurricanes that touched down in the U.S., three of which made landfall along the Gulf Coast and one that hit Massachusetts.
- Named storms: 11 (0.00 more than average)
- Hurricanes: 5 (0.91 less than average)
- Category 3 or higher hurricanes: 2 (0.52 less than average)
A Category 5 hurricane struck Cuba in 1925. This unnamed storm was the first Category 5 hurricane recorded in the database managed by the National Hurricane Center.
- Named storms: 4 (7.00 less than average)
- Hurricanes: 1 (4.91 less than average)
- Category 3 or higher hurricanes: 0 (2.52 less than average)
The 1925 season started late, with the first hurricane beginning on Aug. 18. That season also included a hurricane that made landfall in Florida on Nov. 30, the latest hurricane to hit the U.S.
- Named storms: 11 (0.00 more than average)
- Hurricanes: 8 (2.09 more than average)
- Category 3 or higher hurricanes: 6 (3.48 more than average)
Of the eight hurricanes in the 1926 season, four proved particularly deadly. A storm in July killed 247 people, an August storm killed 25, a September storm killed 372, and a hurricane in October 1926 killed 709.
- Named storms: 8 (3.00 less than average)
- Hurricanes: 4 (1.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
No hurricanes struck the U.S. in 1927. The most significant hurricane of the season was nicknamed The Great August Gales, and it was the deadliest tropical storm to hit Canada in the 1920s.
- Named storms: 6 (5.00 less than average)
- Hurricanes: 4 (1.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
The Okeechobee Hurricane of 1928 was one of the deadliest storms ever to hit the U.S., killing between 2,500 and 3,000 people. The hurricane also hit Puerto Rico, landing on Sept. 13, the feast day of Saint Philip. It is the second hurricane to hit Puerto Rico on this day of celebration.
- Named storms: 5 (6.00 less than average)
- Hurricanes: 3 (2.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
The Great Bahamas Hurricane, also known as the Great Andros Island hurricane, barely moved over the course of three days, hovering above Nassau and Andros in the Bahamas. It was also the first hurricane to approach the Bahamas from a northeast direction.
- Named storms: 3 (8.00 less than average)
- Hurricanes: 2 (3.91 less than average)
- Category 3 or higher hurricanes: 2 (0.52 less than average)
Though 1930 had a quiet hurricane season overall, it also had one of the Atlantic Ocean's deadliest hurricanes. The Dominican Republic Hurricane is the fifth deadliest storm in the region's history. It created a path of destruction up to 20 miles wide and killed between 2,000 and 8,000 people in the Dominican Republic, though it also brought much-needed rain to Puerto Rico.
- Named storms: 13 (2.00 more than average)
- Hurricanes: 3 (2.91 less than average)
- Category 3 or higher hurricanes: 1 (1.52 less than average)
In 1931, a Category 4 hurricane hit Belize, also known as British Honduras, and killed about 2,500 people. It is the deadliest hurricane to hit Belize in recorded history.
- Named storms: 15 (4.00 more than average)
- Hurricanes: 6 (0.09 more than average)
- Category 3 or higher hurricanes: 4 (1.48 more than average)
The Huracn de Santa Cruz del Sur, a Category 4 storm, hit Cuba in 1932 and caused 3,500 fatalities. Most of the deaths were due to a storm surge, a flash flood that rose to over 20 feet.
- Named storms: 20 (9.00 more than average)
- Hurricanes: 11 (5.09 more than average)
- Category 3 or higher hurricanes: 6 (3.48 more than average)
The 1933 season is the Atlantic Basin's third most active hurricane season in recorded history. It also held the record for the highest amount of wind energy created during the Atlantic hurricane season until 2011.
- Named storms: 13 (2.00 more than average)
Read more:
AI and machine learning are improving weather forecasts, but they won't replace human experts - Herald & Review
Read More..