Written by: Olivier Lam, Physical AI Team, Jua.ai AG
Key Takeaways for Energy and Trading Teams
- EPT-2 beats both ECMWF HRES and NOAA GFS on RMSE and CRPS for 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation across the full 0–240 hour lead time range.
- 2025–2026 StationBench verification against over 10,000 ground stations confirms EPT-2 surpasses both traditional models across every lead time and variable combination tested.
- EPT-2 keeps forecast changes more stable from run to run, with standard deviation of forecast changes cut by about 15% versus ECMWF HRES and 25% versus GFS.
- A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves roughly €1.5 million per year through lower imbalance costs and more effective hedging.
- Experience these accuracy gains in your own portfolio by scheduling a benchmark session with Jua to test EPT-2 against your current forecast provider.
ECMWF vs GFS vs EPT-2: What the 2026 Data Shows
The StationBench verification provides definitive answers on forecast accuracy across lead times. ECMWF HRES runs at 9 km resolution with twice-daily updates, while GFS delivers about 27 km resolution forecasts updated every 6 hours. These technical differences explain why ECMWF usually beats GFS, yet EPT-2 still outperforms both across all critical energy variables.
ECMWF traditionally leads GFS in medium-range accuracy, especially for temperature and wind beyond 72 hours. However, EPT-2 surpasses both models across every lead time and variable combination tested. This result marks a clear shift in atmospheric prediction capability for energy use cases.
See the head-to-head comparison in a live EPT-2 demo tailored to your current forecast stack.
Documented Weaknesses of the GFS Model for Energy Use
The 2025–2026 verification confirms several known GFS limitations when compared with ECMWF and modern AI models:
- Coarser spatial resolution: GFS runs at 27 km resolution versus ECMWF at 9 km, which weakens its ability to capture mesoscale weather features that matter for renewable assets.
- Lower run-to-run consistency: GFS shows higher variability between consecutive runs, especially for wind ramp events and convective precipitation.
- Reduced skill in rapid-change scenarios: During fast-moving systems, GFS produces larger timing and intensity errors than ECMWF HRES.
- Limited ensemble depth: GEFS offers 30 perturbed members plus control, while ECMWF runs a 50-member ensemble, which improves probabilistic skill.
These weaknesses become costly in energy trading, where accurate wind and solar radiation forecasts drive portfolio valuation and hedging decisions.
Run-to-Run Consistency and Severe-Weather Performance
Forecast consistency across runs matters as much as raw accuracy for trading decisions. The run-to-run variability described above, one of GFS’s core weaknesses, deserves closer attention because it directly affects position management during volatile periods.
Consistency metrics highlight clear gaps between models during high-impact weather events. Recent AI weather models show stronger severe-event prediction than traditional numerical systems for tropical cyclones, atmospheric rivers, and extreme temperatures.
EPT-2 keeps run-to-run variability lower than both ECMWF and GFS, with the consistency advantage mentioned earlier proving crucial during volatile weather. Traders gain a steadier forecast signal, which supports more confident position sizing and fewer reactive adjustments.
For severe weather, EPT-2 improves detection of wind ramp events above 10 m/s per hour. Hit rates reach 18% higher than ECMWF HRES and 28% higher than GFS across European wind-rich regions during winter 2025–2026.
Using Ensembles from EPT-2e and ECMWF in Trading
EPT-2e, Jua’s 30-member ensemble, beats the 50-member ECMWF ENS mean on RMSE and CRPS at nearly every lead time. Traders can turn this edge into more precise probabilistic hedging strategies.
The ECMWF ensemble reaches anomaly correlation of about 0.8–0.9 at 5–7 day lead times, which sets a strong benchmark. EPT-2e delivers anomaly correlations of 0.85–0.92 over the same window, with particular strength in wind and temperature that drive renewable output.
The difference in ensemble depth, with 30 members for EPT-2e and 50 for ECMWF ENS, shows that model design and training can outweigh raw member count when delivering probabilistic skill for energy trading.
Trading Impact: From Accuracy Gains to P&L
Forecast accuracy gains flow directly into trading economics. The four-percentage-point accuracy gain mentioned earlier translates to about €1.5 million in annual savings per GW of wind capacity. For solar portfolios, the same improvement delivers roughly €3 million in yearly savings per GW of installed capacity.
Verification results show EPT-2 improving accuracy by 6–8 percentage points over GFS and 3–4 percentage points over ECMWF HRES across key energy variables. For a typical European utility with 2–3 GW of renewables, these gains create about €9–15 million in annual value.
Beyond direct savings, better forecast consistency cuts the number of costly position changes during volatile weather. EPT-2’s stable run-to-run behavior reduces false signals and supports more durable trading strategies in stressed markets.
How Jua for Energy Works with Your Existing ECMWF Setup
Jua for Energy enhances an existing ECMWF-based workflow rather than replacing it. Most advanced trading desks keep their ECMWF subscription and route EPT-2 and EPT-2e through the Jua platform for rapid-refresh updates and transparent cross-model comparisons.
The platform hosts more than 25 models, including ECMWF HRES, ENS, and AIFS alongside the EPT family. This mix enables real-time benchmarking and model consensus tracking. Traders can watch forecast divergence between EPT-2 and ECMWF and flag opportunities when models disagree on key variables.
EPT-2 updates as often as 24 times per day, while ECMWF runs twice daily. The higher update frequency gives intraday refreshes that capture evolving weather between traditional NWP cycles. This timing edge proves especially useful during fast-moving systems that drive intraday power price swings.
Schedule a workflow review to see how EPT-2 fits alongside your current ECMWF-based tools.
Frequently Asked Questions
Which model is more accurate at 5–7 day lead times?
ECMWF HRES usually outperforms GFS at medium-range lead times, with better scores for temperature, wind, and precipitation beyond 72 hours. The gap grows at 5–7 days, where ECMWF’s higher resolution and data assimilation provide clear benefits. EPT-2’s superiority extends to this medium-range window, achieving lower RMSE values for all energy-critical variables. For ensembles, ECMWF ENS still beats GEFS, while EPT-2e delivers stronger probabilistic performance than both traditional ensemble systems.
Which model performs better for wind-ramp events in 2026?
Wind-ramp prediction remains one of the hardest parts of energy forecasting and has major impact on grid stability and trading. Verification for 2026 shows EPT-2 achieving the highest hit rates for wind ramps above 10 m/s per hour, with 18% better performance than ECMWF HRES and 28% better than GFS. The gain comes from EPT-2’s native any-Δt forecasting, which predicts at arbitrary time intervals instead of rolling forward in fixed 6-hour steps. This design helps EPT-2 capture the timing and intensity of rapid wind changes that move power markets.
How does EPT-2 compare with ECMWF and GFS on surface solar radiation?
Surface solar radiation forecasts sit at the core of solar generation planning and day-ahead positioning. EPT-2 delivers better CRPS scores than both ECMWF HRES and GFS across all lead times, with standout performance from 72 to 240 hours where traditional models lose skill. This accuracy edge supports more reliable solar generation forecasts, sharper intraday trading, and lower imbalance risk.
EPT-2’s cloud cover and radiation fields show lower bias and higher correlation with ground observations, especially during partly cloudy conditions. These conditions create the toughest scenarios for solar portfolio management and the largest payoff from improved forecasts.
Conclusion and Practical Next Steps
The 2025–2026 verification results position EPT-2 as a new benchmark in atmospheric prediction. It surpasses ECMWF HRES and NOAA GFS across every energy-critical variable and lead time combination. Combined with faster updates and stronger run-to-run consistency, this shift changes what energy traders can expect from weather guidance.
For traders, meteorologists, and quants, the impact reaches beyond accuracy into workflow efficiency and risk control. Integrating EPT-2 alongside existing NWP models strengthens decision-making without forcing a full system overhaul.
Run a live benchmark on the Jua platform to test EPT-2 on your regions and variables and see results in under five minutes.