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ECMWF vs GFS: Which Weather Model Should You Use for Polymarket Trading?
Every serious Polymarket weather trader eventually faces the same question: when ECMWF is pointing one direction and GFS is pointing another, which one do you trust? This guide goes deep on both models — where each excels, where each fails, and the practical decision framework for daily temperature trading.
Why This Question Matters
In a Polymarket daily temperature market, your profit comes from identifying the gap between the true probability of each bucket and the price the market assigns. Your probability estimate is only as good as the forecast it’s built on.
If you use GFS when ECMWF is substantially more accurate for a given city or regime, your probability estimates will be systematically worse than your competitors’ — which means your edge calculations will be wrong, and trades you think are +EV will be negative EV. Over hundreds of trades, that’s the difference between a profitable operation and a slow bleed.
Model selection is not a background detail. It’s one of the three or four highest-leverage decisions in a weather trading strategy.
The Models: A Technical Overview
GFS (Global Forecast System)
The GFS is operated by NOAA’s National Centers for Environmental Prediction (NCEP) and has been in continuous operation since 1980. It is the primary global model of the US government.
- Resolution: 13 km globally (0.25°) at analysis level; 28 km beyond day 10
- Vertical levels: 127 pressure levels from surface to ~80 km
- Update cycle: 4× daily — 00, 06, 12, 18 UTC
- Forecast horizon: 16 days deterministic, 35 days ensemble (GEFS, 31 members)
- Access: Fully public and free via NOAA NOMADS and Open-Meteo
GFS is the backbone of most free weather data services — Open-Meteo, Visual Crossing, most phone weather apps. Any weather data service that provides free forecasts runs on GFS or a GFS-derivative.
ECMWF IFS (Integrated Forecasting System)
The ECMWF IFS is operated by the European Centre for Medium-Range Weather Forecasts in Reading, UK. It is widely considered the world’s best operational global model by most objective metrics.
- Resolution: 9 km globally (0.1°) for the deterministic HRES run
- Vertical levels: 137 pressure levels from surface to ~80 km
- Update cycle: 4× daily; primary runs are 00 and 12 UTC
- Forecast horizon: 15 days deterministic (HRES), 15 days ensemble (ENS, 51 members)
- Access: Paid for real-time. Open-Meteo provides ECMWF output free with a ~6 hour lag.
ECMWF data costs money. Full high-resolution output requires a commercial subscription (~$1,000–$10,000+ annually). Open-Meteo’s free lag version is sufficient for non-latency-critical strategies.
Where ECMWF Wins
Medium-Range Accuracy (Days 3–10)
Beyond 3 days, ECMWF has consistently outperformed GFS on virtually every objective metric — anomaly correlation coefficient (ACC), RMSE, Brier skill score — for decades. A 5-day GFS forecast is roughly as accurate as a 4-day ECMWF forecast.
Polymarket implication: For markets opening 4–7 days before the target date, ECMWF is the primary model to use. GFS probabilities at this range are systematically wider than they should be — leading to underpricing of the modal bucket and overpricing of the tails if you use GFS alone.
Europe and Asia
ECMWF has particularly strong performance over Europe and Asia. Its data assimilation system ingests a broader array of global observations, and its higher native resolution resolves smaller-scale features in complex coastal and mountainous terrain. For Polymarket’s European markets (London, Paris) and Asian markets (Tokyo, Hong Kong, Seoul, Shanghai), ECMWF is consistently the better primary model.
Ensemble Reliability
The ECMWF ENS (51 members) produces better-calibrated probabilistic forecasts than GEFS (31 members) at most lead times and regions. Better calibrated means: when ECMWF ENS says 30% for a bucket, it’s right roughly 30% of the time. For bucket-probability estimation — the core quantitative task in weather trading — this calibration difference matters directly.
Where GFS Wins
Final-Day US Markets with HRRR
Inside the 18-hour window for US markets, the HRRR (High-Resolution Rapid Refresh) — a NOAA model running hourly at 3 km resolution — substantially outperforms both. GFS wins by association here: both GFS and HRRR are fully public and free, while ECMWF’s free version via Open-Meteo arrives 4–6 hours late — acceptable for 24–48h lead time but not for the final few hours.
Price and Accessibility
For a bootstrapped operation, GFS via Open-Meteo or NOAA NOMADS is free, immediate, and complete. As the operation scales and ECMWF access fees become trivially small relative to P&L, upgrading to real-time ECMWF is worthwhile. For a trader starting out, GFS via Open-Meteo is the correct choice.
Model Recommendations by City
| City | Primary Model | Notes |
|---|---|---|
| New York | HRRR (final day), ECMWF (24–72h) | HRRR dominates at <18h; ECMWF for 3–5 day markets |
| Los Angeles | HRRR (marine layer), ECMWF | HRRR hourly updates critical on marine-layer days |
| London | ECMWF IFS HRES | ECMWF's home turf; Met Office UKV adds 1.5 km UK detail |
| Paris | ECMWF IFS HRES | Météo-France AROME at 1.3 km for best Le Bourget precision |
| Tokyo | JMA MSM, then ECMWF | JMA model is highest skill for Japan — often ignored by Western bots |
| Shanghai | ECMWF, then CMA | CMA model access is limited; ECMWF is practical primary |
| Hong Kong | ECMWF | HKO issues excellent local text guidance — integrate it |
| Seoul | KMA, then ECMWF | KMA is Korea-specific; partially accessible via third parties |
When the Models Disagree
The most interesting situation for weather traders is when ECMWF and GFS point in different directions — particularly in transitional seasons ahead of major weather systems.
Quantify the Disagreement
Don’t just note that the models disagree — express each model’s forecast as a probability distribution over the relevant buckets. The disagreement is meaningful if the two distributions assign substantially different probabilities to the modal bucket (e.g., ECMWF puts 45% on 22–23°C, GFS puts 25% on the same bucket), or the implied temperatures differ by more than 1.5°C for a 1°C-bucket market.
Do Not Bet Into a Coin Flip
When ECMWF and GFS disagree substantially, your model-implied edge is not reliable. A situation where ECMWF says 65% for bucket A and GFS says 65% for bucket B is not a 65% situation — it’s a “we don’t know” situation. The correct response is usually to pass or place a smaller-than-normal position at a higher edge threshold.
Wait for Convergence
If the disagreement is on a 3–4 day market, consider waiting 12–24 hours for the next model runs. When subsequent runs from both models converge toward one solution, that convergence is a tradeable signal — the first trader to recognize it can enter before the market reprices.
The AI Model Question: GraphCast, Pangu, AIFS
In 2023–2024, a wave of AI-based forecast models emerged with impressive benchmark performance. For Polymarket weather trading, the relevant question is whether they improve bucket-level probability estimation.
Current evidence: AI models perform well on large-scale pattern metrics but are more mixed on 2-meter temperature at specific station locations. They tend to produce smoother, averaged outputs — systematically underpredicting extremes because their training loss penalizes large individual errors. This matters because tail events are disproportionately important for tail-bucket trading.
Practical takeaway: AI weather models are worth including in a multi-model ensemble, but not as a replacement for ECMWF or GFS. They add diversity but systematically underperform on the extreme values where the largest market mispricings often occur.
The Practical Stack
Level 1: Free ($0/month)
- Open-Meteo GFS ensemble (31 members, free, ~2–4 hour lag)
- Open-Meteo ECMWF (HRES + ENS, free, ~4–6 hour lag)
- Open-Meteo NBM (US only, free, hourly updates)
- NOAA HRRR via Open-Meteo (US only, free, hourly)
This covers all Polymarket cities with multi-model ensemble blending at zero cost. The lag is acceptable for 24–48h lead-time strategies.
Level 2: Paid ECMWF (~$200–$500/month)
Real-time ECMWF API eliminates the 4–6 hour lag, enabling latency-based strategies around the 00/12 UTC primary runs — the most consistently documented edge window in Polymarket weather trading.
Level 3: Advanced (~$500–$2,000/month)
Real-time ECMWF + JMA MSM for Asia + Met Office UKV for London + Météo-France AROME for Paris + HRRR at 1-hour intervals for US cities + live METAR feed. The stack a serious multi-city operation runs.
The Bottom Line
ECMWF is better at medium range (3–10 days), globally, especially for European and Asian markets. GFS (and HRRR for US markets) is better when free and real-time access matters, inside 24 hours for US stations, and as a cross-check.
The traders who have generated sustained profits in Polymarket weather markets are not the ones who identified the “best” single model. They’re the ones who correctly modeled uncertainty across multiple forecasts — and priced that uncertainty into their trades before the market caught up.