How AI is Transforming Weather Forecasting & Climate Resilience 

Harnessing AI to predict, prepare, and protect against extreme weather.

AI is revolutionizing weather forecasting—offering faster, more cost-effective, and increasingly accurate predictions. But its success depends on how we integrate it with existing forecasting systems, ensure public accessibility, and bridge global gaps in meteorological infrastructure.

From devastating wildfires to record-breaking storms, extreme weather events are becoming more frequent and intense. 2024 was the hottest year on record and disasters such as Los Angeles’ wildfires and Hurricane Beryl highlight the urgency of improved forecasting and disaster response.

The ICEF AI for Climate Change Mitigation Roadmap explores high-potential opportunities for using AI to fight climate change. In their chapter on "Extreme Weather Response," Alice C. Hill and Colin McCormick highlight groundbreaking advancements in weather forecasting. They also discuss the challenges of equitable access, data limitations, and the need for continued public-private collaboration.

AI’s Impact on Weather Forecasting

For years, researchers have explored how AI could improve weather forecasting. In 2023, Google’s GraphCast and Huawei’s Pangu-Weather achieved major breakthroughs. They showed that AI-driven forecasts outperformed some traditional numerical weather prediction (NWP) models in both accuracy and speed.

What’s novel about the latest AI-based models? 

• Traditional models rely on complex physics-based models run on supercomputers.  They deliver accurate predictions but require intensive computing power, expert meteorology teams, and high operational costs.

• AI-based models analyze past weather data to determine cause-and-effect relationships between weather conditions. This allows them to spot subtle trends and patterns in data, which they can then extrapolate to predict what’s coming.

The results are impressive.

Google’s GraphCast accurately predicted Hurricane Beryl’s shift toward Texas earlier than some leading traditional models. Huawei’s Pangu-Weather generates forecasts up to 10,000 times faster than traditional models. Also, both models use significantly less energy, by comparison, over their lifetimes despite the extensive training required upfront.

AI’s Role in Disaster Preparedness

Improved forecasting can greatly enhance disaster response and preparedness by providing earlier warnings and real-time risk assessments. Potential applications include:

• Flood Prediction & Response: Analyzing rainfall patterns and topography to identify flood risks early, helping cities implement better evacuation plans.

• Wildfire Risk Assessment: Assessing patterns in temperature, wind, and vegetation dryness to predict wildfire likelihood and help firefighters allocate resources.

• Hurricane & Storm Tracking: Interpreting real-time updates on storm paths and intensities, helping communities prepare.

• Tornado Warnings: Detecting tornado formation earlier than conventional systems, improving warning times.

By integrating AI-powered forecasting into emergency response systems, communities can reduce casualties and minimize economic damage.

Public Infrastructure is Still Required

Weather forecasting—both traditional and AI-driven—relies on publicly funded meteorological infrastructure. Agencies like NOAA (U.S.), ECMWF (Europe), and WMO (global) operate the satellites, weather stations, and climate databases supplying the critical raw data used in forecasting models. Decades of public investment have built a system that delivers continuous, high-quality observations essential for predicting weather events.

This publicly collected data remains indispensable to both traditional numerical weather prediction (NWP) models and emerging AI-driven approaches. While private companies like Google and Huawei currently provide free access to their AI-powered forecasts. 

There is no guarantee this will remain the case. If access becomes restricted, the benefits of AI-enhanced forecasting (such as, earlier and more precise extreme weather warnings) could shift from being a public good to a privatized resource.

Key Challenges & Recommendations

Despite AI’s demonstrated ability to improve forecasting, ICEF identifies several challenges to broaden and maintain adoption as well as recommendations for how to address them:

Trust & Adoption: Overcoming Resistance to AI-based Forecasting

AI models often outperform traditional forecasting in speed and accuracy. Yet, many emergency responders and policymakers hesitate to rely on them without clear validation. Critical decisions (such as ordering evacuations or mobilizing resources) require a high degree of confidence and cannot be made lightly.

➡️ To drive adoption, governments and research institutions must establish validation protocols, and enforce transparency standards. They must also train meteorologists to integrate AI-driven forecasting into disaster preparedness. Public trust will grow when models are explainable, reliable, and consistently proven in real-world scenarios.

Global Infrastructure & Equity: Ensuring AI Forecasting Benefits Everyone

AI-driven forecasting depends on reliable data, but many regions (especially low-income countries) lack the necessary weather stations, satellite networks, and AI expertise. Without investment, these areas will remain underserved, limiting access to life-saving early warnings.

➡️ Governments, international agencies, and private companies must: 

• Expand meteorological infrastructure.

• Develop AI models suited for low-data environments.

• Scale early warning systems.

Public vs. Private Control: Keeping AI Forecasting a Public Good

AI-enhanced forecasting relies on publicly funded data, yet private companies are developing the most advanced models. If access becomes restricted, critical early warnings may be unavailable to those who need them most.

➡️ Governments must safeguard public access by investing in open-source forecasting models. They should also maintain open data policies and regulate AI model accessibility to prevent a shift toward for-profit forecasting that excludes vulnerable communities.

Looking Ahead

With the right investment and policies, AI can become a cornerstone of climate resilience. This equips governments, emergency responders, and communities with faster, more accurate forecasts. 

Extreme weather and climate change have become a growing threat to vulnerable populations. Enhancing early warning systems, optimizing disaster preparedness, and improving response coordination creates AI-driven insights that mitigate risks and save lives.

Want to Learn More?

Explore the ICEF AI Climate Roadmap for a deeper look at how AI is transforming climate resilience, forecasting, and disaster preparedness. You can also see how well it’s driving innovation in emissions reduction and sustainable energy solutions.

Read the report here: Roadmap | Innovation for Cool Earth Forum (ICEF)

About the Author

Bill Stark is passionate about solving climate challenges and is inspired by those leading the way at the intersection of AI and climate. With a background in scaling businesses that tackle these issues, he’s especially interested in AI’s potential to accelerate progress across the broader climate landscape. Through this newsletter, he shares what he’s learning—hoping to celebrate those at the forefront, spark conversations, and encourage more people to apply their talents to create a more sustainable future.

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