Google revolutionizes weather forecasting by combining generative AI and diffusion models, and does so with its innovative SEEDS (Scalable Ensemble Envelope Diffusion Sampler) system. This development marks a turning point in the way we anticipate climate change and extreme events, allowing forecast accuracy and efficiency to reach new levels, beyond those achieved by traditional supercomputers, and at a fraction of their cost.
What is SEEDS and why is it disrupting climate prediction?
The importance of SEEDS lies in that addresses two major historical challenges in meteorology:
- The computing cost: allows for the creation of many more possible forecasts (called “ensembles”) using only a small fraction of a supercomputer’s capacity.
- Uncertainty in extreme events: SEEDS is able to simulate and anticipate rare and extreme weather phenomena from a much broader database of possible scenarios.
According to Google Research, SEEDS achieves greater statistical confidence by generating a much larger number of plausible weather trajectories, reducing the risk of error in critical situations such as heat waves, torrential rains, hurricanes and other severe phenomena.
This is how SEEDS works: artificial intelligence at the service of precise meteorology
The heart of SEEDS lies in the so-called probabilistic diffusion models: an advanced AI methodology that, rather than being limited to a single outcome or "best guess," generates a multitude of possible scenarios based on one or two initial forecasts. It uses noise-removal algorithms, similar to those used by generative AI models in other areas, such as image creation or natural language (for example, ChatGPT or Gemini).
This conceptual similarity with generative AI allows SEEDS, unlike traditional systems, produce dozens of alternative predictions for the same initial situationTo put it simply, it's as if we could ask thousands of "virtual experts" what will happen and compile statistics based on their responses, thereby increasing the certainty of our forecast.
The most notable advantages of this approach are:
- Increased forecast generation speed: Up to 256 times faster than traditional methods, facilitating real-time responses.
- Minimum computational consumption: It is estimated that only 10% of the power required by a supercomputer to run the same number of simulations with classical numerical models is sufficient.
- Better characterization of extreme events: increases the ability to anticipate infrequent but high-impact events with much greater reliability.
As Google engineers explain, SEEDS learns from globally collected physical data (atmospheric pressure, humidity, temperature, wind, etc.) and is capable of extrapolating dozens of future possibilities, helping meteorologists better understand and communicate risks.
Comparison: How does SEEDS improve on traditional prediction methods?
- Traditional methods: They use physics-based models that, to calculate probabilities, require running the same model dozens of times with small variations in initial conditions (ensemble forecasting). This is extremely expensive and usually only feasible in large meteorological centers with almost unlimited resources.
- SEEDS: starts with a few “seed” forecasts and, through generative AI, creates a whole cloud of possible scenarios, saving up to 90% of computing resources and allowing more institutions (not just national agencies) to access advanced predictions.
Thanks to this, the system can be democratized for local and regional uses, or even in sectors such as agriculture, emergency management, renewable energy, and logistics, where it would currently be unfeasible due to hardware costs.
Practical and potential real-world applications of SEEDS
- High-resolution meteorology: SEEDS allows more predictions to be released at shorter intervals, increasing the frequency and quality of alerts.
- Climate risk management: facilitates early detection of potential heat waves, heavy rains, floods or unusual events, helping to save lives and minimize material damage.
- Resource optimization for meteorological agencies: Savings in computing infrastructure can be used to improve the resolution of physical models, purchase additional sensors, or extend the forecast window.
- Impact on strategic sectors: Energy, agriculture, logistics, aviation, and tourism can benefit from more reliable, hyperlocal predictions.
- Support in communicating alerts: Automated systems can, thanks to the variety of scenarios, anticipate with greater certainty when to issue critical warnings to the population.
How does SEEDS transform the study of extreme weather events?
One of the fundamental challenges in meteorology is to anticipate rare and high-impact phenomena, such as warm air intrusions, severe storms, flash floods, or prolonged droughts. Traditional models, since they only simulate a limited number of scenarios, can fail to "capture" these events. SEEDS, by expanding the range of possible atmospheric trajectories, greatly improves the ability to predict such eventualities.
For example, in sudden heat waves caused by intrusions of warm air from North Africa into Europe, SEEDS proved capable of reproducing the probability and spatial structure of the phenomenon more accurately, helping emergency services and citizens prepare early and accurately.
In the context of climate change and the increasing frequency of extreme events, tools such as SEEDS are crucial for preventive planning and reducing the impact on infrastructure, agriculture and public health.
Closely linked to other Google AI models: MetNet-3, GraphCast and future integration
Google's commitment to climate-related artificial intelligence goes far beyond SEEDS. The model already integrates and coexists with two key technologies:
- MetNet-3: Specialized in hyperlocal and short-term forecasts (nowcasting) with high resolution, ideal for anticipating heavy rain or events in windows of up to 24 hours.
- GraphCast: a generative graph-based model for global and medium-term (up to ten days) predictions, capable of identifying wind formation patterns, cyclones and other large-scale meteorological situations.
The synergy between SEEDS, MetNet-3, and GraphCast will enable progress toward intelligent meteorology, where AI can adapt to issues as varied as renewable energy planning, global logistics, and emergency management for hurricanes and typhoons.
Advantages and differential characteristics of SEEDS
- Drastically reduced computing costs: Up to 90% fewer resources required compared to traditional numerical prediction models, making it feasible to extend to regions and countries without major infrastructure.
- Scalability and democratization: Any institution, university or local agency can integrate SEEDS and obtain world-class predictions.
- Improvements in statistical reliability: By increasing the number and diversity of generated scenarios, the estimation of probabilities improves, especially in extreme events.
- Compatibility with physical systems: SEEDS can coexist with classical physical models, acting as a "statistical emulator" that multiplies the available information without the need for costly additional calculations.
- Based on real data, advanced training: The system has been trained and validated with skill metrics such as rank histogram, ECM and CRPS, outperforming the reliability of traditional Gaussian systems.
How does SEEDS influence daily life and key sectors?
- Citizenship: Access to more accurate forecasts allows you to plan everyday activities, trips, or outdoor events with greater confidence.
- Emergency managers: improves the response capacity to floods, storms or forest fires by better anticipating the probabilities of occurrence.
- Energy sector: Renewable energy (mainly wind and solar) depends on reliable weather forecasts to plan generation and consumption.
- AgricultureCrop and irrigation optimization is now much more precise, reducing losses and improving profitability in the sector.
- Tourism and aviation: Flights, events, and travel can be adapted based on reliable, real-time weather information.
The key to the SEEDS revolution is in its ability to learn and multiply possible predictions, overcoming the bottleneck represented by computational costNow, even modest institutions can access cutting-edge technology, and countries with fewer resources can anticipate climate threats with equal or greater reliability than the most advanced meteorological centers.
Furthermore, new possibilities are opening up for increasing the frequency of warnings, refining the spatial resolution of forecasts, and moving toward proactive and personalized meteorology, key to the digital society and the challenges of climate change.
The innovation behind SEEDS is a clear example of the potential of generative AI applied to real-world challenges: Accurately predicting the weather is no longer just a matter of powerful supercomputers, but of creativity in algorithms and global accessibility.Google is thus expanding the frontier of meteorological science, providing tools with enormous social, economic, and environmental impact, making the world better prepared for the climate challenges of today and tomorrow.