In a breakthrough for weather prediction technology, Google has unveiled SEEDS (Scalable Ensemble Envelope Diffusion Sampler) – a groundbreaking generative AI system designed to revolutionize extreme weather forecasting capabilities.
Leveraging state-of-the-art AI and machine learning, SEEDS operates similar to large language models like ChatGPT by rapidly generating multiple high-fidelity weather scenarios, promising faster and more economical forecasts.
The Dire Need for Precise, Timely Extreme Weather Prediction
Accurate weather forecasting is crucial for saving lives and minimizing the catastrophic impacts of natural disasters like hurricanes, heatwaves, wildfires, and floods. However, it has long been hampered by the sheer complexity of atmospheric variables including pressure, temperature, humidity, wind patterns and more. Traditional physics-based forecasting methods frequently struggle to predict the severity and trajectories of extreme weather events with precision.
While effective, generating probabilistic ensemble forecasts from physics-based models is also highly computationally intensive, requiring supercomputers to run complex numerical simulations multiple times. Consequently, many operational forecasts can only afford relatively small ensembles of 10-50 members, severely limiting the ability to accurately characterize rare but high-impact extreme events.
How Google’s Innovative SEEDS Overcomes Existing Limitations
SEEDS tackles these forecasting challenges head-on by tapping into the power of generative AI models. It can generate large ensemble forecasts conditioned on just one or two initial forecast inputs from an operational weather prediction system. The generated ensemble members not only yield plausible real-weather patterns but also match or exceed physics-based ensembles in crucial verification metrics while characterizing extreme tails more accurately.
A key advantage is SEEDS’ ability to capture detailed spatial correlations and cross-variable relationships, generating realistic weather maps and modeling joint distributions – a capability that simplistic data-driven approaches lack.
Most importantly, SEEDS’ computational cost is negligible compared to traditional ensemble methods, with the ability to generate 256 high-resolution 2-degree ensemble members in just 3 minutes on Google Cloud TPUs.
Rigorous Real-World Validation
SEEDS has already proven its potential through rigorous real-world testing across extreme weather scenarios like the 2022 European heatwave. While traditional methods failed to anticipate the full severity, SEEDS retrospectively provided accurate predictive insights days in advance by better quantifying the probabilities of such rare events occurring.
During the heatwave’s peak on July 14th, 2022, SEEDS generated a 16,384-member ensemble that successfully enveloped the observed temperature and moisture extremes around Lisbon – an event so unlikely that none of the 31 members from the operational U.S. forecast ensemble predicted it just 7 days prior.
The Future of Climate-Resilient Forecasting
As climate change intensifies extreme weather globally, Google’s SEEDS technology offers transformative potential for enhancing preparedness and resilience through improved predictive capabilities at scale. By empowering advanced assessment of high-impact event likelihoods, SEEDS can save lives, safeguard communities, and drive sustainability efforts.
While SEEDS currently augments traditional numerical weather prediction as a post-processing technique, Google envisions it spearheading an AI-enabled paradigm shift. The computational savings enabled by this generative AI approach could ultimately be reinvested into running higher-resolution physics-based models more frequently, ushering in a new era of rapidly updating, hyper-local ensemble weather intelligence harnessed to solve the existential challenges posed by the climate crisis.