How AI helps predict extreme weather | BBC News

Summary

In this episode of AI Decoded, experts discuss the revolutionary impact of AI on weather forecasting. Oxford University's Dr. Shuyi Chen highlights the development of a digital twin of Earth, a supercomputer model that simulates climate change scenarios. The European Center's Florence Rabier and the UK Met Office's Professor Steven Belcher emphasize AI's role in enhancing accuracy, particularly in predicting extreme events. They also touch on citizen science initiatives, encouraging public participation in data collection to combat climate change and improve forecasting.

Takeaways

• 🌐 AI is revolutionizing weather forecasting by processing vast amounts of data and creating highly detailed models of the Earth's climate system.
• 🔍 Oxford University is developing a 'digital twin of the Earth' to simulate natural phenomena and human activities, aiding in the prediction of extreme weather events.
• 🌡️ AI models are improving the accuracy of weather predictions, particularly in tracking tropical cyclones, with an increase of about 25% in accuracy.
• 🌍 The European Center for Medium-Range Weather Forecasts (ECMWF) collaborates with 35 countries to run global models that predict weather patterns worldwide.
• 💻 AI technology allows for the creation of high-resolution, physics-based models that can predict weather down to specific urban areas, such as differentiating temperatures across London.
• 🌪️ AI is particularly useful in predicting severe weather events, which are becoming more frequent and intense due to climate change.
• 🌤️ The UK Met Office is using AI to enhance weather forecasts with fine details, such as temperature variations within urban areas, by incorporating machine learning techniques.
• 🌿 Citizen scientists can contribute to climate research by submitting local weather data, which can be used to improve models and predictions.
• 🌎 AI is being used to not only predict immediate weather but also to model long-term climate change scenarios, helping to plan for the future.
• 🛠️ Despite advancements, AI models are not perfect and have areas for improvement, such as predicting the intensity of typhoons, which is currently less accurate.

Q & A

What is the significance of AI in weather forecasting?

AI is revolutionizing weather forecasting by processing vast amounts of data and making predictions more accurate. It enables the creation of digital twins, which are highly detailed models of the Earth that can simulate different climate scenarios, helping scientists predict the evolution of climate change.

How does the digital twin of the Earth aid in climate prediction?

The digital twin of the Earth is a super model that encompasses all knowledge of the physics of the atmosphere and the Earth system. It is run on supercomputers and allows for the simulation of natural phenomena and human activities, providing highly detailed, interactive data that supports decision-making around extreme weather events.

What role does cloud computing play in making AI-generated weather forecasts more accessible?

Cloud computing allows for the storage and sharing of AI-generated weather forecasts, making them readily available to governments and aid agencies. This technology enables regions with limited resources to access sophisticated weather forecasting tools, which can be run on a laptop rather than requiring a supercomputer.

How has the collaboration between Oxford University and the UN World Food Program improved weather prediction in the Horn of Africa?

Oxford University has developed an AI system in collaboration with the UN World Food Program that pulls together current and historic data to provide localized weather forecasts. These forecasts are accessible on a website and are generated using AI models that can run on local equipment, enhancing the ability of governments and aid agencies to prepare for climate disasters.

What is the impact of AI on the accuracy of predicting tropical cyclones?

AI models have shown to be approximately 25% more accurate in predicting the track of tropical cyclones, such as typhoons and hurricanes. However, they are still being improved upon, as they are about 20% less accurate in predicting the intensity of these storms.

How does the UK Met Office use AI to enhance weather forecasting?

The UK Met Office uses AI to add fine detail onto routine forecasts, providing high-resolution data on temperature and rainfall. This is achieved by combining crowdsourced data with machine learning tools, which allows for the production of very detailed forecasts that can inform anticipatory actions, especially in urban areas where heat islands can exacerbate weather conditions.

What is the 'weather on the web' initiative mentioned in the script?

The 'weather on the web' initiative is a platform where citizens can submit their local weather observations, such as temperature and rainfall, to contribute to weather forecasting. This crowdsourced data is then used by organizations like the UK Met Office to improve the accuracy and detail of their forecasts.

How has the accuracy of weather forecasts improved over the past decades?

Over the last 50 years, the accuracy of weather forecasts has improved tremendously due to advancements in satellite observations, global models, and increased computational power of supercomputers. The improvement is often measured by the increase in the forecast range, with the 4-day forecast now as accurate as the 3-day forecast was a decade ago.

What is the potential of citizen scientists in contributing to weather and climate studies?

Citizen scientists play a crucial role by contributing local data through initiatives like 'weather on the web' and other crowdsourcing platforms. This data helps scientists to better understand and predict weather patterns and climate change, ultimately aiding in the fight against these global challenges.

How does the EU's Copernicus program document and predict climate change?

The Copernicus program uses historical weather and climate data to document changes in temperature, storm frequency, and other climate indicators. It also uses these models to predict future climate scenarios based on different scenarios of greenhouse gas emissions, particularly carbon dioxide.