Inside Spire’s AI-driven sub-seasonal-to-seasonal model

For energy or commodity traders, natural gas producers, agronomists, or government emergency or resource planners, the safety and security of your investment – whether in people or product – relies on weather forecasting.

Physics-based models lose forecast skill between weeks 3-6, a timeframe that is aptly named the “valley of uncertainty.” Spire’s AI-S2S model rewrites the outcome for customers who are prepared. AI-S2S runs a 200-member generative AI ensemble out to 46 days, trained on Spire’s own satellite data, and outperforms ECMWF by 14.76%* exactly where your next season’s book is exposed.

Register to join Spire scientists and engineers for a live look inside Spire AI-S2S. Our team will break down the methodology, the benchmarks, and what Spire AI-S2S means for planning further ahead in energy, agriculture, and government.

Register for the session that works best for you:

Who should attend a session?

If your P&L, your margin, your cargo or your supply commitment moves with the weather a month out, you’ll leave the session with insights on how to move quicker and more confidently than your competitors. The following individuals will specifically find this session helpful:

• Energy and commodity traders looking for a confident, consistent answer to “how cold, how likely, and for how long — four weeks out?”
• Government emergency managers looking to pre-position people and assets ahead of worst-case headlines instead of reacting to them.
• LNG traders and cargo optimization managers wanting to better estimate whether cargo travels east or west.
• Natural gas & propane producers who want answers to “do I fill storage now, or wait two more weeks?”
• Commodity & grain traders who are tracking weather across hemispheres and want to understand how global weather events reprice basis.
• Agronomists and digital ag product leads looking to plan field trafficability across fields.
• Energy planners looking to predict and forecast reserve across wind and solar generation.
• Insurance underwriters trying to price a season before it starts.
• Chief risk officers evaluating buying signals.

In each session we’ll cover the following, with localized market examples:

• What makes Spire AI-S2S different — model architecture, methodology, and independence of every public S2S model
• How it performs: accuracy benchmarks vs. ECMWF S2S*
• Real-world use cases across energy, agriculture, and government operations
• A live hindcast and data demo
• What’s coming next for AI-S2S and Spire’s AI forecast portfolio
• Q&A session

Our speakers