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Artificial intelligence plays an important role in climate forecasting as it is developing rapidly and has the potential to revolutionize these forecasting methods.

AI algorithms analyze large amounts of historical weather data and can identify patterns that are difficult or impossible for humans to detect, meaning more accurate and faster forecasting capabilities than traditional models.

Machine learning and deep learning

Machine learning techniques push the capabilities of artificial intelligence weather forecasting to greater heights by training its algorithms on historical weather data to learn how to predict future weather conditions. These algorithms can determine the complex relationships between various meteorological variables such as temperature, pressure and humidity.

Another important technology is “deep learning,” algorithms that can analyze large amounts of data without human intervention. Deep learning algorithms have been shown to be particularly effective in forecasting complex weather events such as hurricanes and storms.

A successful prediction story

In 2023, artificial intelligence models were able to predict the path of Hurricane Lee a week before its occurrence and with greater accuracy than traditional forecasting models.

Hurricane Lee formed in the Atlantic Ocean on September 20, 2023, and the storm quickly intensified to a Category 4. The hurricane then turned north and began approaching the New England coast.

On September 25, the National Hurricane Center (NHC) issued a hurricane warning for coastal areas of Massachusetts and Rhode Island. The center expected Lee to make landfall near Nantucket, Massachusetts on September 26. Later that day, Lee turned slightly eastward and made landfall in Nova Scotia, Canada as a Category 2 hurricane, causing extensive damage without major loss of life.

How did Lee’s prediction come about?

This is thanks to Google AI’s “Grabcast” model, a “deep learning” model that is trained on large data sets of satellite images and other weather data. The model can identify patterns in this data that are difficult for humans to detect.

In the case of Hurricane Lee, GraffCast was able to identify a pattern in the storm’s motion as it moved northward and approached the coast of New England. This pattern is not immediately apparent to humans, but GraphCast has detected it and used it to predict the storm’s path with great accuracy.

“Stock – weather” model

In addition to Google AI’s graph cast model, Huawei Cloud is developing another AI-powered weather model. The model can predict up to a week in advance, just as accurately as traditional forecasting methods, but much faster.

“Stock – Weather” uses a “deep learning” algorithm to learn the relationships between various weather variables such as temperature, pressure and wind speed. It can identify patterns in data that are too complex for traditional forecasting methods to detect.

Modeling – can generate highly accurate forecasts of weather for specific locations, which can be used to make informed decisions about things like agriculture, transportation and disaster preparedness. It is also used by the European Center for Meteorological Forecasting (ECMWF) to improve its forecasts.

Thunderstorms forecast

In 2022, researchers at the University of California, Berkeley developed an artificial intelligence model that could predict thunderstorms six hours in advance with 80 percent accuracy. This model is called the Thunderstorm Forecast System (TFS). It was trained on the National Oceanic and Atmospheric Administration’s (NOAA) dataset of more than 10 million thunderstorm observations.

TFS works by analyzing a variety of data sources, including satellite imagery, radar data, and ground-based weather stations. It uses this data to identify patterns and trends associated with thunderstorm formation. Once TFS detects these patterns and trends, it can use them to predict the formation, intensity, and duration of thunderstorms in a given location. TFS is still under development, but it will be a valuable tool for forecasting thunderstorms and improving the accuracy and timeliness of thunderstorm warnings.

Wider benefits

The application of artificial intelligence to weather forecasting benefits a wide range of fields. It helps farmers determine the best time to plant and harvest their crops.

This helps airlines plan their flights more efficiently and avoid dangerous weather conditions. Energy companies can generate and distribute electricity more efficiently based on artificial intelligence advice. Insurance companies can assess risks and set premiums more precisely.

Weather forecasting using AI is still in its infancy, but it has already made significant progress. While AI-based predictive systems are currently being used, they are expected to play a greater role in the future.