GFS Ensemble Weather Trading

What Is GFS and Why Does It Run 31 Simulations?

The Global Forecast System is NOAA's operational numerical weather prediction model. It runs four times per day, producing forecasts out to 16 days. But the standard GFS product most weather apps show you is just one of those runs — the “control” run with best-estimate initial conditions.

The GFS ensemble is different. NOAA runs 31 slightly perturbed versions of the same model, each starting from a slightly different initial state. Why? Because the atmosphere is a chaotic system. Small differences in the current state of the atmosphere compound over time. By running 31 perturbations, NOAA captures the range of plausible future states — the forecast uncertainty.

When all 31 members agree, the forecast is high-confidence. When they spread apart, the atmosphere is in a sensitive state where small uncertainties compound into large outcome differences. That spread is information.

Ensemble Spread Indicates Forecast Uncertainty

Ensemble spread — the range of temperatures across all 31 members — is a direct measure of forecast confidence. A tight cluster of members all predicting a high temperature between 68°F and 72°F signals high confidence. A spread from 58°F to 80°F signals a chaotic atmospheric pattern where even the best models cannot agree.

For trading, high uncertainty is usually a signal to stay out. If the 31 GFS members straddle a Kalshi threshold, the true probability is near 50% and there is no edge regardless of the market price.

Low uncertainty with strong directional agreement is the trading opportunity. When 28 out of 31 members agree the temperature will exceed the threshold, the model probability is 90%. If the Kalshi market prices it at 75%, the spread is 15 points in your favor.

Converting Ensemble Output to Probability Distributions

Converting 31 temperature values into a probability estimate for a specific threshold is a counting problem at its simplest. Count the members where the predicted temperature exceeds the Kalshi threshold. Divide by 31. That is your GFS probability.

In practice, the pipeline is more involved: pulling member forecasts from the Open-Meteo API at city coordinates, interpolating to the correct forecast hour, and aggregating across the daily window to match how Kalshi defines “daily high.” But the core logic is the count.

What AIGEFS Adds: AI Architecture and Project EAGLE

In December 2025, NOAA launched Project EAGLE — the AI Ensemble Forecast System (AIGEFS). It is built on Google DeepMind's GraphCast architecture, retrained on NOAA's own historical observational data. Like GFS, it runs as 31 ensemble members.

GraphCast represents a fundamentally different approach to weather prediction. Instead of solving the partial differential equations that govern fluid dynamics forward in time — the approach traditional models like GFS use — GraphCast uses a graph neural network trained to predict how the atmospheric state evolves. It learns from data rather than from physics first principles.

This architectural independence is the key property for ensemble trading. GFS errors and AIGEFS errors are not perfectly correlated. When both systems err, they tend to err differently. Combining them into a grand ensemble produces better calibrated probabilities than either system alone.

HGEFS: Combining Physics and AI for 62 Members

The Hybrid Global Ensemble Forecast System (HGEFS) is NOAA's official framework for combining the GFS and AIGEFS ensembles. The combined probability from 62 members is computed as a simple aggregate: each member gets one vote on whether the temperature exceeds the threshold.

NOAA's own verification studies show HGEFS outperforms both GFS-only and AIGEFS-only systems on temperature forecast skill scores at the 1-7 day range. That is precisely the window that matters for Kalshi daily and weekly contracts.

Predict & Profit uses the full 62-member HGEFS ensemble. This is the core methodology described in detail on the how-it-works page.

Agreement vs. Disagreement: How It Affects Confidence

When the GFS ensemble probability and the AIGEFS ensemble probability agree closely, confidence in the combined estimate is high. A GFS probability of 0.82 and AIGEFS probability of 0.79 produces a combined 0.81 with high confidence.

When the two ensembles diverge significantly — GFS at 0.82, AIGEFS at 0.48 — the combined probability is near 0.65, but more importantly, the disagreement itself is a warning signal. The physics model and the AI model are giving very different answers about the same atmospheric state. That disagreement reflects genuine uncertainty that neither ensemble is capturing internally.

Predict & Profit requires a minimum level of agreement between the two ensemble systems before considering a trade. When they disagree significantly, the trade is skipped regardless of the individual probabilities. This alone eliminates a meaningful fraction of positions that look attractive on one metric but are genuinely uncertain.

Practical Example: 28 of 31 Members Predict DC > 55°F

How This Compares to What Most Traders Use

The typical Kalshi weather trader checks the NWS point forecast or Weather.com: one number, one prediction, no uncertainty estimate. If that forecast says 57°F for the high and the Kalshi threshold is 55°F, the trader buys YES. They have no probability estimate — they have a binary “yes it will” or “probably not.”

The 62-member ensemble does not give one answer. It gives a distribution of 62 answers from two independent modeling approaches and computes a probability. A single NWS point forecast cannot tell you whether a contract is priced at fair value or 15 points wide. An ensemble can. That is the edge.