The Role of AI and ML in Streamlining Weather Forecasting

Applied Tech Review | Wednesday, January 04, 2023

Weather forecasters are integrating AI and ML tools with weather forecasting models to make better predictions.

FREMONT, CA: Artificial intelligence and machine learning are proving to be very useful for weather prediction, as they are in many other fields of science. Machine learning has shown some potential in our research on forecasting high-impact weather. Researchers also believe these tools open up new possibilities for better forecasts where more than human expertise is required.

There still are major challenges with employing just human expertise. It remains difficult to predict tornadoes, large hail, or heavy rain during thunderstorms. Inaccurate predictions trigger subsequent inaccuracies in predicting small changes in complex processes, making weather less predictable.

Models: Numerical weather prediction models are today's primary tools for weather forecasters. Weather stations, balloons, and satellites provide observations of the current state of the atmosphere for these models, which solve equations governing air movement.

It is possible to predict most weather systems with these models, but the smaller the event, the harder it is to do. Integrating weather prediction models with human analysis leads to better predictions. Forecasters are remarkably good at analyzing the huge amount of weather data they receive daily, but their memory and bandwidth could be improved. Machine learning and artificial intelligence can help with some of these challenges.

Artificial intelligence (AI): These tools allow forecasters to make predictions beyond models' capabilities, such as high-impact weather. A machine learning approach is also being incorporated into numerical weather prediction models to speed up tasks that can be computationally intensive, such as predicting how water vapor turns into rain, snow, and hail.

Machine learning (ML): In the future, machine learning models may replace traditional numerical weather forecasting models. These systems process past weather maps instead of solving complex physical equations as models do to learn how weather systems typically behave. They then make weather predictions based on historical data and current weather conditions.

Research has shown that machine learning-based forecast systems can predict general weather patterns and numerical weather prediction models with only a fraction of the computing power. Researchers are closely testing these new tools and developing new methods for forecasting local weather. Although they still need to provide detailed information about local weather, these tools have promising prospects.

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