TLDR: Can Artificial Intelligence Help Predict Australia’s Weather?

Weather forecasters and warning services are experimenting with artificial intelligence (AI) to aid in predicting and warning of extreme weather events, such as floods and tropical cyclones. AI models can quickly analyze large datasets and provide localized information, allowing authorities to make more accurate predictions and take appropriate actions. However, some researchers caution that AI-based models might not be able to improve upon numerical models that provide a range of probabilities. Additionally, the accuracy and effectiveness of AI models are still being tested and refined. Nevertheless, AI has the potential to revolutionize weather forecasting and prediction, leading to improved services and better preparation for extreme weather events.

Key Points:

• Weather forecasters and warning services in Australia are using AI to predict and warn of extreme weather events.
• AI models can quickly analyze large datasets to provide localized weather information and predictions.
• The accuracy and effectiveness of AI models are still being tested and refined.
• AI has the potential to revolutionize weather forecasting and prediction.

Kerry Plowright, the owner of Early Warning Network, uses data generated by his own firm to detect and issue alerts on extreme heat, rainfall, and flooding. The firm is starting to test AI models that promise to make a lot more weather information available rapidly and at low cost. Similarly, FloodMapp uses machine learning to analyze flood data and provide more time for communities to prepare. The models complement those of the Bureau of Meteorology (BoM) and help authorities in decision-making. AI models are also being explored for other applications, such as spraying crops and providing forecasts in remote regions of Australia.

However, some researchers caution that AI-based models might not be able to improve upon existing numerical models that provide a range of probabilities. AI models inherit and potentially extrapolate imperfections of the traditional models they train on. Nevertheless, AI has the potential to improve weather forecasting and prediction, especially in areas that lack good meteorological organizations. For example, Google’s GraphCast forecast model has outperformed traditional models in predicting tropical cyclones, atmospheric rivers, and extreme temperatures. The reduced cost and energy requirements of AI models could also make weather forecasting more accessible and affordable in the future.

Despite its potential, researchers believe that AI still has limitations in weather forecasting, especially in predicting the effects of a changing climate. The accuracy and reliability of AI models in such complex scenarios need further research and refinement. However, AI has value in aiding in predicting extreme events and providing better preparation for changing weather patterns.