Short Intro
In an exciting breakthrough for weather prediction, the European Centre for Medium-Range Weather Forecasts (ECMWF) has transformed its forecasting system to run more than ten times faster while slashing the energy needed by a factor of one thousand. This achievement ensures quicker, more reliable weather forecasts for everyone—from emergency planners and farmers to the general public—while also reducing the environmental impact of supercomputing.
How ECMWF Got Here
ECMWF is renowned for its high-resolution, global forecasting model. Until recently, running a single detailed forecast could take hours on a large array of traditional CPUs, and the power demands were enormous. Facing rising energy costs and growing concerns about carbon footprints, the ECMWF team set out to redesign their computing approach from the ground up.
Revolutionizing Forecast Speeds
The key innovation lies in moving the core of the Integrated Forecasting System (IFS) to graphics processing units (GPUs). GPUs excel at handling many simple calculations in parallel, unlike central processing units (CPUs), which are optimized for complex, sequential tasks. By rewriting critical code sections in a GPU-friendly form and applying mixed‐precision arithmetic, ECMWF scientists achieved a tenfold speed‐up. What once required eight hours on a CPU cluster now completes in under forty minutes on the new GPU‐powered system.
Cutting Energy Use by 1,000
GPUs not only provide vast computational power but also do so with lower energy per calculation. By carefully balancing precision and performance, the ECMWF team cut the energy required per forecast by around 1,000 times compared with their CPU‐only setup. This massive reduction comes from three factors: using GPUs with higher performance-per-watt, reducing data movement within the system, and optimizing algorithms to avoid unnecessary calculations.
Behind the Scenes: Technical Highlights
1. Code Refactoring
• Critical loops and data‐heavy routines were rewritten to run efficiently on GPUs.
• Mixed‐precision techniques (combining 16-bit and 32-bit arithmetic) preserved forecast accuracy while reducing compute time.
2. Data Assimilation
• The system that merges observations from satellites, radars, and weather stations was adapted to the new hardware.
• Advanced compression methods and parallel I/O streams ensured data could be fed into the model without bottlenecks.
3. Collaboration and Testing
• ECMWF worked with hardware vendors, software tool developers, and academic partners.
• Rigorous validation against historical cases confirmed forecast quality was maintained or improved.
Real-World Impact
Faster, greener forecasts bring tangible benefits:
• Emergency managers receive quicker warnings about severe storms or floods.
• Energy companies can better predict demand spikes driven by weather.
• Farmers gain timely guidance to protect crops from frost or drought.
• Researchers can run more model experiments to study climate change scenarios.
What’s Next for ECMWF?
Buoyed by this success, ECMWF plans to:
• Scale up to even higher resolution forecasts, capturing weather features as small as a few hundred meters.
• Integrate machine learning techniques to refine parameterizations and deliver localized forecasts.
• Share best practices and open‐source code so other weather centres can adopt energy‐efficient forecasting.
Three Key Takeaways
• Speed and Sustainability Combined: ECMWF’s move to GPU‐based computing speeds forecasts by over ten times while cutting energy usage by around 1,000x.
• Cutting‐Edge Code and Collaboration: Refactoring core forecast routines, mixed‐precision arithmetic, and partnerships with hardware and software experts made this leap possible.
• Broad Benefits: Faster, greener forecasts support emergency planning, agriculture, energy management, and climate research, demonstrating the value of sustainable high‐performance computing.
Three-Question FAQ
Q1: How does using GPUs speed up weather forecasts?
A1: GPUs handle many simple calculations in parallel. By rewriting key forecasting code to run on GPUs and using mixed‐precision math, ECMWF achieved far higher computational throughput than on traditional CPUs.
Q2: Is forecast accuracy affected by the energy‐saving measures?
A2: No. ECMWF carefully tested mixed‐precision techniques and algorithm optimizations against a wide range of historical weather events. The new system matches or improves the accuracy of previous forecasts.
Q3: Can other meteorological centres adopt these advances?
A3: Absolutely. ECMWF is committed to sharing open‐source tools, code snippets, and best practices so that national weather services and research institutions around the world can benefit from faster, greener forecasting.
Call to Action
Stay ahead of the storms and join the push for sustainable science. Visit www.ecmwf.int to learn more about their GPU‐accelerated forecasts and sign up for weekly updates. Share this story with your colleagues and help spread the word about how high‐performance computing can serve people and the planet.
