Harnessing AI for Superior Weather Prediction
Harnessing AI for Superior Weather Prediction
AI is revolutionizing diverse aspects of our lives, and weather prediction is no exception. This article explores how this cutting-edge technology is being utilized in Australia to enhance weather forecasting accuracy and timeliness, especially in the face of extreme weather events.
Late last year, Kerry Plowright, founder of Early Warning Network, was alerted by his own firm's AI system of an impending hailstorm. This early warning allowed Plowright to protect his vehicles, demonstrating the potential of AI in providing accurate and timely weather alerts. Private entities have always utilized data from established weather agencies like the Bureau of Meteorology (BoM) or the European Centre for Medium-Range Weather Forecasts (ECMWF). However, Early Warning Network is trailblazing the use of AI models to provide a wealth of weather information swiftly and affordably.
Juliette Murphy, founder of FloodMapp, is enthusiastic about the prospects of AI in weather prediction. After witnessing devastating floods in Queensland and Calgary, she established FloodMapp to provide communities with advanced warning and preparation time. This AI-powered solution utilizes machine learning and traditional physics-based models to analyze vast datasets and predict flood impacts rapidly.
AI is not only being embraced by private entities. The BoM has been actively exploring AI capabilities for several years, aiming to improve its services for the government, emergency management partners, and the public.
Justin Freeman, former head of BoM's research team and current founder of Flowershift, is leveraging AI to fill the gaps in current weather forecasting products. Flowershift's AI model, trained on existing observational data, can provide forecasts in remote regions of Australia and beyond, offering more flexibility and accessibility. AI models' ability to analyze data cost-effectively and provide localized information opens up numerous potential applications. For instance, farmers could use these models to determine the best time to spray their crops, ensuring optimum yield.
Despite the exciting prospects, there is also caution regarding reliance on AI-based models for weather prediction. These models, like Google's GraphCast or Nvidia's FourCastNet, are based on traditional models and hence, inherit their imperfections. They may not always improve on numerical models that offer a range of probabilities. However, they do offer benefits, such as the ability to provide finer resolution in predicting weather changes.
One of the most challenging areas of weather prediction is climate change, where the task is harder due to less data and the need to predict well outside the norm. AI can be valuable here, helping to predict extreme events in a changing climate. However, it is crucial to recognize and address the limitations of AI-based models to harness their full potential effectively.
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