The Role of Generative AI in Climate Tech

This article is the first part in a three-part series titled, “The Role of Generative AI in Climate Tech”. Stay tuned as we uncover how generative AI could be one of our most powerful tools in combating climate change.

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In recent years, generative AI has become widely recognized for its remarkable ability to create realistic images, art, and even videos. But beneath the surface of media buzz lies, generative AI can drive meaningful change in sectors beyond the content and art—particularly in climate technology.

Far from being just a tool for creativity, generative AI has evolved into a robust solution for complex, data-driven challenges. For those working at the intersection of technology and sustainability, this evolution is not only exciting but essential for driving impactful climate solutions.

What is Generative AI doing in Climate Tech?

Climate change presents a data challenge: it involves processing massive datasets across disciplines, from meteorology to environmental science. Traditional weather prediction models rely on physics-based equations and predefined parameters. While these models are well-suited for short-term forecasts, they struggle with long-term predictions due to the chaotic nature of weather and climate data.

Generative AI (GenAI), on the other hand, can recognize intricate dependencies between variables, potentially allowing it to make more refined predictions over longer timescales. GenAI in climate tech leverages deep learning models to analyse and simulate vast amounts of data, offering unique insights that were previously difficult or even impossible to attain using conventional methods. GenAI models, trained on these diverse data sets, can predict weather patterns and provide real-time assessments for climate impact.

How much better is GenAI compared to what we already have?

Conventional weather prediction systems are trained for months and sometimes years, to produce a 10-days prediction in hours.

GraphCast is Google DeepMind’s AI weather forecasting model and it can accurately predict weather in seconds, not hours. It took only 4 weeks to train the AI model to parse through almost 40 years worth of historical data, and it resulted in making more accurate 10-day predictions in under a minute, compared to the 6 hours needed by the HRES (developed by European Centre for Medium-Range Weather Forecasts) to do the same thing, for comparison.

The AI model’s accuracy significantly beats current weather systems on 90% of 1,380 metrics for weather prediction. It is also about 1,000 times more energy efficient than conventional weather forecasting models.

The difference in speed is revolutionary, and it’s only possible due to AI.

In climate technology, GenAI is not merely a tool for making artistic content. It is an engine for generating insights, optimizing resources, and supporting informed decision-making on a global scale. Up next in this series is a deep dive of NVIDIA Earth-2’s revolutionary tech for typhoon prediction in Taiwan and ending with a look towards a future where climate data is accessible to all. 

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Nika is also using GenAI to ease the processes of building geospatial models, maps and retrieving climate and spatial insights. Whether it's inquiry or comment, we'd love to hear your thoughts on this space. Drop us a quick DM for a friendly hello!