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Can AI help us predict extreme weather?

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The AI revolution has transformed meteorology, with AI models such as Google's Graph Cast being able to predict weather faster, but the role of human meteorologists in communicating uncertainty remains crucial. AI models, trained on historical data, can miss rare outlier events and may struggle to predict weather changes further into the future. However, the integration of AI and traditional forecasting methods has the potential to enhance weather prediction and emergency management.

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Hurricane Le, a category 3 storm, made landfall in Nova Scotia, Canada, causing significant damage.
Forecasters predicted the storm's trajectory, but Google's AI model provided a more accurate prediction 3 days in advance.
The AI model's prediction of the cyclone's path demonstrated the advancement of technology in meteorology.
Weather forecast accuracy is crucial for preparing and responding to extreme weather events.
Weather prediction requires collecting data from various sources and using supercomputers to analyze the information.
Data collection involves satellite images, weather stations, and buoys to gather information about the atmosphere.
Supercomputers process the data to generate a 3D grid model of the atmosphere and produce weather forecasts.
Local meteorologists refine the global models based on their specific area's features and conditions.
Ensemble forecasting is used to measure the uncertainty of weather predictions.
Multiple forecasts are generated to assess the variation and certainty of the prediction.
Ensemble forecasting helps meteorologists understand the potential for upcoming weather events.
AI models have the capability to predict weather changes more frequently and rapidly.
AI in weather forecasting is based on a large historical data set and can make predictions faster than traditional models.
Researchers have developed AI models that can predict weather by being trained on a huge historical data set.
AI models can produce fast and accurate forecasts for variables like surface temperature, humidity, and wind speed.
AI models still have challenges in predicting the intensity of extreme weather events.
AI weather models can produce forecasts quickly but may struggle to predict rare outlier weather events.
AI models are incentivized to prioritize safer correct answers, potentially causing them to miss rare outlier events.
Historical weather data used to train AI models may not fully capture the frequency of extreme events in the future due to climate change.
AI models can generate larger ensembles of forecasts, allowing for better measurement of uncertainty in weather predictions.
AI forecasting models can provide better quantification of uncertainty in weather predictions, but the role of human meteorologists in communicating and understanding uncertainty remains crucial.
AI models may improve the ability to forecast subseasonal time scales and quantify uncertainty more effectively.
The role of human meteorologists in communicating and understanding uncertainty is essential and is not expected to change.
The European Center for medium-range weather forecasting has started publishing AI forecasts for the public to compare with traditional ones.