12/05/2024 / By Richard Brown
faTraditionally, climate scientists have relied on statistical methods to analyze climate data, but a recent breakthrough is transforming this approach. Researchers now hope to use artificial intelligence to revolutionize weather forecasting.
A team led by Etienne Plesiat of the German Climate Computing Center in Hamburg, alongside researchers from the United Kingdom and Spain, applied artificial intelligence (AI) to reconstruct past climate extremes across Europe. (Related: Climate change data is based on FRAUD, and scientists around the world are pushing back against the narrative.)
The research confirmed known climate trends and uncovered previously unrecorded extreme events that traditional methods missed. The team used historical simulations from the CMIP6 archive (Coupled Model Intercomparison Project) to train an AI model called CRAI to reconstruct past climate data.
The results were validated using standard accuracy metrics, but some experts caution that AI-generated outputs must be rigorously tested due to the potential for inaccuracies like hallucinations.
AI hallucinations happen when AI perceives patterns or objects that are nonexistent or imperceptible to human observers. This creates outputs that are nonsensical or altogether inaccurate.
Meanwhile, at the University of Oxford, climate scientists have successfully developed an AI-based weather model that enhances rainfall forecasts, offering high-resolution predictions without the need for costly supercomputers.
“This approach is a game-changer for regions with limited resources but which are increasingly affected by climate change,” said Shruti Nath, a climate scientist at Oxford.
By collaborating with the European Centre for Medium-Range Weather Forecasts, the team integrates AI and machine learning with physical atmospheric models to deliver advanced weather forecasts tailored to specific regional needs.
Accurate early warnings play a critical role in disaster risk management. With reliable forecasts, governments and humanitarian organizations can take anticipatory actions, such as deploying resources or relocating vulnerable populations, before disasters occur. This proactive approach not only saves lives but also reduces the economic costs associated with disaster relief.
The initiative benefits from the support of Google and Google Cloud, whose funding and computational resources are essential for overcoming the infrastructure constraints faced by meteorological agencies in East Africa. These contributions enable the use of AI models to make accurate predictions, even in resource-limited regions.
National meteorological agencies, such as the Kenya Meteorological Department (KMD) and the Ethiopia Meteorological Institute, are vital partners in testing and refining these AI-powered forecasts.
“We are actively using these machine learning forecasts to compare them with our current operational methods. The results show significant improvements in accuracy,” said Hannah Wangari, Assistant Director at KMD.
The regional climate center ICPAC is leading efforts to build the forecasting capacity of 11 East African countries that are being affected by extreme weather. By ensuring local ownership and trust, ICPAC helps integrate AI technology into existing systems. This collaboration is critical for making AI-powered weather forecasting accessible and effective for local communities.
The success of this initiative in East Africa has global implications. The project aims to expand AI-driven forecasting models to other regions facing similar climate challenges.
By continuing to refine AI technology and forming new partnerships, the goal is to create a more resilient global community capable of anticipating and responding to the worsening impacts of weather events. AI has the potential to revolutionize weather forecasting and early warning systems, empowering communities worldwide to better prepare for the unpredictable challenges posed by extreme weather.
Watch this video exploring how geoengineering is one of the biggest factors affecting wrong weather forecasting.
This video is from the Alex Hammer channel on Brighteon.com.
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