GenAI’s Real-World Impact: Predicting Taiwan’s Typhoons
- Denada Permatasari
- Jan 24
- 3 min read
This article is the third part in a four-part series titled, “The Role of Generative AI in Climate Tech”. Read the first and second part. The final part is coming soon! Subscribe to our newsletter to be notified when it comes out!
Previously, we touched on how NVIDIA Earth-2 works to make fine resolution output from low-resolution input data. In this article, we’re diving into a real use case of NVIDIA Earth-2 to showcase the real impact it has to save lives.
Recently, Earth-2 was used to improve typhoon modeling in Taiwan, one of the most typhoon-prone regions in the world.
Taiwan, renowned as one of the wettest locations globally with an annual rainfall ~3x the worldwide average, faces an average annual disaster cost of $650M.
This financial burden is caused by seasonal typhoons, resulting in extensive flooding that damages life and property and requires large-scale evacuation efforts.
Traditional models often struggle to capture the rapid intensification or erratic paths of typhoons, phenomena that depend on resolving small-scale interactions within the atmosphere and ocean.
At a 25km resolution, the path and shape of the typhoon are so fuzzy and vague, and disaster response couldn’t be more timely and precise due to the vagueness of the incoming threat.
The righthand graphic is Earth-2 at work, converting ERA5 data at 25 km to 2 km data around Taiwan. The model works on this finer resolution, yet it simulates typhoon behavior 1,000 times faster and 3,000 times more energy efficient with unparalleled precision compared to conventional models.
Taiwan's Central Weather Administration (CWA) plans to employ these diffusion models to anticipate more precise sites of typhoon landfall. Chia-Ping Cheng, the administrator of CWA, explained the importance of using Earth-2 in the context of Taiwan being a significant link in the global supply chain. With over 136 typhoons hitting the island since 2000, deploying Earth-2 to dilute these impacts is crucial to enhance the quality and resolution of disaster informatics.
Immediately, the difference is immense. NVIDIA Earth-2 has provided critical insights into potential flooding zones and infrastructure risks. This data was instrumental for local governments and emergency response teams, helping them allocate resources more effectively and saving lives.
Building a Future-Ready Climate Tech Ecosystem
By generating much finer resolution using the same data that is already available, GenAI used in climate modeling has fundamentally broken the industry notion that we need to wait for better datasets and better data storage that only big institutions can afford, for more accurate and almost real-time predictions. Ingenuity makes the leap forward.
NVIDIA Earth-2 isn’t just a faster climate model—it’s a glimpse into the future of democratised climate tech. By drastically reducing the computational barriers to high-resolution modeling, GenAI opens doors for policymakers and even non-specialists to engage with climate data. This paradigm shift paves the way for a more inclusive approach to tackling climate challenges, where solutions are informed by data but driven by communities.
As we look ahead, the potential to make advanced climate modeling tools as accessible as a smartphone app could redefine how we understand and respond to the planet's most pressing issues. Stay tuned for the final article in this series, where we’ll map how GenAI is laying the future for climate modeling and its implications.
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. Drop us a quick DM for a friendly hello!
This article is the third part in a four-part series titled, “The Role of Generative AI in Climate Tech”. Read the first and second part. The final part is coming soon! Subscribe to our newsletter to be notified when it comes out!
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