Summarize this content to 2000 words in 6 paragraphs in Arabic Stay informed with free updatesSimply sign up to the Artificial intelligence myFT Digest — delivered directly to your inbox.Artificial intelligence-powered forecasts as fast and accurate as those generated by the best equipped public weather services could be delivered from desktop computers around the world under an international project unveiled on Thursday.The developers of the Aardvark prediction model hope it will “democratise” the AI-driven weather forecasting revolution by bringing it within reach of countries with fewer resources in Africa and elsewhere. The details were published in Nature.The initiative is led by the UK’s Alan Turing Institute, with partners including Cambridge university, European Centre for Medium-Range Weather Forecasts and Microsoft. It comes amid a flurry of big technical advances in weather forecasting announced by companies such as Google DeepMind and Nvidia as well as public meteorological offices in recent months.“Aardvark’s breakthrough is not just about speed, it’s about access,” said Scott Hosking of the Turing Institute. “By shifting weather prediction from supercomputers to desktop computers, we can democratise forecasting, making these powerful technologies available to developing nations and data-sparse regions around the world.”Researchers in public and private sectors are using machine learning to make predictions from millions of worldwide weather observations rather than crunching the data in supercomputers with conventional physics-based equations. But these new AI systems generally require intensive processing to assimilate the data and set the initial conditions before each run. In contrast, Aardvark is an “end-to-end” model. It dispenses with the compute intensive first step and works directly on observations from satellites, weather stations and other sensors, generating both global and local forecasts. That cuts the energy and processing power required by a factor of many thousands.Although Aardvark is still in its experimental phase, an evaluation by its developers showed that it outperformed the US Global Forecast System on many variables. The team is working to deploy Aardvark in regions poorly served by the global weather models generated by meteorological centres in the industrialised world, particularly in Africa.Aardvark could be transformative in west Africa if adapted to “local contexts” to give more precise information on phenomena such as intense rain systems, said Amadou Gaye, professor of climate physics at Senegal’s Université Cheikh Anta Diop Dakar. That would help better predict both damaging flooding episodes and precipitation patterns that affect the harvesting of crops such as peanuts, said Gaye, who has been working with the Turing Institute. He compared the potential impact to the arrival of mobile phones, which had delivered fundamental services such as banking — and weather forecasts — to farmers. Meteorological conditions make it easier to forecast the weather a few weeks into the future in Africa than in temperate regions of the northern hemisphere, because the continent’s large-scale weather patterns are much more stable, said Richard Turner, project leader at the Turing Institute. “There’s a big opportunity here for AI to make ‘sub-seasonal’ forecasts four to six weeks ahead, which has rather been ignored in the north because it is so hard to do there — a big win for instance for African agriculture,” he added.Although Microsoft was an industrial partner in the project, the company does not plan to commercialise Aardvark, Turner said. “Everything is completely open source.” Suzanne Gray, professor of meteorology at Reading university, who is not involved in the project, said: “Aardvark’s demonstrated capability to produce both global and local station forecasts directly from observations . . . is hugely impressive and showcases the massive potential of machine learning in this weather forecasting.”But she said more development was needed for Aardvark to generate all the variables and fine spatial resolution needed for the forecasts and weather warnings issued routinely by public bodies such as the UK Met Office.
