Weather Prediction and AI

What was the significant achievement of Google's AI model in predicting Hurricane Lee?

• It predicted the hurricane's impact on power outages.

• It predicted the hurricane's landfall location precisely.

• It predicted the hurricane's path three days before traditional models.

• It predicted the hurricane's intensity accurately.

What is the primary method used by traditional weather forecasting to predict future weather?

• Using historical weather patterns.

• Applying physics equations to a 3D grid of the atmosphere.

• Relying on meteorologists' intuition.

• Using AI models to analyze data.

What is ensemble forecasting?

• A method to predict weather using AI models.

• A technique to measure uncertainty by generating multiple forecasts.

• A way to predict weather using historical data.

• A method to predict weather using satellite images.

What is a key advantage of AI models over traditional weather forecasting methods?

• They require less data to make predictions.

• They can produce forecasts much faster.

• They are more accurate in predicting hurricane intensity.

• They do not rely on any observation data.

How do AI models learn to predict weather?

• By applying physics equations to the atmosphere.

• By using satellite images to predict patterns.

• By analyzing historical weather data sets.

• By relying on meteorologists' expertise.

What is a limitation of AI models in weather prediction?

• They cannot predict weather more than a day in advance.

• They struggle with predicting the intensity of hurricanes.

• They require more computing power than traditional models.

• They are less accurate than traditional models.

What is a potential benefit of larger ensemble forecasts in AI models?

• They can predict weather events with absolute certainty.

• They can measure uncertainty more effectively.

• They can predict weather without any data.

• They can replace meteorologists entirely.

What is the main challenge AI models face due to historical weather data?

• Historical data has fewer extreme events than current weather.

• Historical data is too large for AI models to process.

• Historical data is not available for AI training.

• Historical data is too complex for AI to analyze.

Why is the role of meteorologists still important despite advancements in AI?

• They can predict weather more accurately than AI.

• They are essential for communicating uncertainty and making decisions.

• They are required to validate AI predictions.

• They can operate AI models more efficiently.

What is the European Center for Medium-Range Weather Forecasting doing with AI forecasts?

• They are not using AI forecasts at all.

• They are using AI forecasts exclusively for extreme weather events.

• They are publishing AI forecasts alongside traditional ones for comparison.

• They are replacing traditional forecasts with AI forecasts.