Written by: Olivier Lam, Physical AI Team, Jua.ai AG | Last updated: June 27, 2026
Key Takeaways for Energy Traders
- ECMWF HRES remains the global benchmark for numerical weather prediction, with strongest accuracy in the 0–48 h window and progressively weaker skill at longer lead times.
- Across 2 m temperature, wind, and precipitation, ECMWF HRES consistently beats NOAA GFS at every lead-time band, with the largest gap in the 48–120 h trading horizon.
- Jua’s EPT-2 model outperforms ECMWF HRES on every energy-critical variable (10 m wind, 100 m wind, 2 m temperature, surface solar radiation) from 0–240 h, while EPT-2e surpasses the ECMWF ENS mean on both RMSE and CRPS.
- EPT-2 updates up to 24 times per day at a fraction of traditional NWP compute cost, so traders act on fresher forecasts and capture measurable P&L gains of up to €1.5 M per GW of wind and €3 M per GW of solar annually.
- See how ECMWF, GFS, and EPT-2 perform on your own region and variables. Run a live benchmark against 25+ models in under 5 minutes.
How Accurate Is ECMWF for Hourly Energy Decisions?
ECMWF’s two-week outlook is the definitive reference point for traders repricing risk around heating demand, renewable output, and system tightness. That authority is earned. ECMWF HRES has led NWP for over forty years, and its verification record is the benchmark every competing model faces. The table below summarizes representative HRES accuracy by lead-time band and variable, drawn from ECMWF’s published verification pages. RMSE values indicate operational skill at each band; exact figures vary by season, region, and initialization cycle.
Table 1: ECMWF HRES indicative accuracy by lead-time band and variable | Variable | 0–48 h (RMSE, indicative) | 48–120 h (RMSE, indicative) | 120–240 h (RMSE, indicative) | | --- | --- | --- | --- | | 2 m temperature (°C) | ~1.5–2.0 | ~2.5–3.5 | ~4.0–5.5 | | 10 m wind speed (m/s) | ~1.8–2.2 | ~2.5–3.2 | ~3.5–4.5 | | 100 m wind speed (m/s) | ~2.0–2.5 | ~2.8–3.6 | ~3.8–5.0 | | Precipitation (mm/6 h) | ~2.5–4.0 | ~3.5–5.5 | ~5.0–7.5 |HRES accuracy is strongest in the 0–48 h window, where initialization quality dominates. Error growth accelerates between 48 h and 120 h as synoptic uncertainty compounds. Beyond 120 h, deterministic skill degrades substantially, a well-documented property of chaotic atmospheric dynamics.
The ECMWF ensemble mean reaches a higher global 500 hPa geopotential height anomaly correlation coefficient at day 10 than the deterministic HRES, which confirms that probabilistic ensemble products carry more useful signal at extended ranges. For energy variables such as 100 m wind at turbine hub height and surface solar radiation, HRES skill at 120–240 h supports directional positioning but not precise generation dispatch.
How ECMWF Compares to GFS for Trading Decisions
Having established ECMWF HRES as the operational benchmark, traders naturally compare it to the free alternative from NOAA. NOAA’s Global Forecast System (GFS) is the free deterministic baseline the energy industry uses alongside ECMWF. The two models share the same NWP methodology but differ in resolution, data assimilation, and ensemble design.
ECMWF HRES runs at 9 km native resolution, while GFS runs at approximately 13 km. The accuracy gap is consistent across variables and lead times, with ECMWF outperforming GFS on every energy-relevant metric at every lead-time band. The table below compares the two on the same variables as Table 1.
Table 2: ECMWF HRES vs. NOAA GFS indicative accuracy by lead-time band and variable | Variable / Lead-time band | ECMWF HRES RMSE (indicative) | NOAA GFS RMSE (indicative) | | --- | --- | --- | | 2 m temperature, 0–48 h (°C) | ~1.5–2.0 | ~2.0–2.8 | | 2 m temperature, 48–120 h (°C) | ~2.5–3.5 | ~3.2–4.5 | | 10 m wind, 0–48 h (m/s) | ~1.8–2.2 | ~2.2–2.8 | | 10 m wind, 48–120 h (m/s) | ~2.5–3.2 | ~3.0–4.0 | | Precipitation, 0–48 h (mm/6 h) | ~2.5–4.0 | ~3.0–5.0 | | Precipitation, 48–120 h (mm/6 h) | ~3.5–5.5 | ~4.5–6.5 |From an energy-trading standpoint, the ECMWF advantage over GFS matters most in the 48–120 h window. This day-ahead and multi-day horizon governs gas storage positioning, cross-border flow nominations, and renewable-generation contracts. GFS remains useful as a second opinion and as a free baseline for markets where ECMWF access is cost-prohibitive.
Neither model, however, updates more than four times per day. That ceiling comes from the compute economics of NWP. A single traditional NWP simulation consumes approximately 8,400 kWh and costs €1,000–€20,000 to run on HPC infrastructure, so traders work with stale numbers between runs.
Who Has the Most Accurate Hourly Forecast in 2026?
Jua is a foundation model and agent company, and Jua for Energy is its first applied product. The underlying model, EPT-2, is the flagship variant of the Earth Physics Transformer (EPT) family, a general spatiotemporal transformer foundation model. EPT-2 outperforms ECMWF HRES on every lead time and on 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation across the full 0–240 hour range.
EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Both results appear in the peer-reviewed technical report arXiv:2507.09703.
Table 3: EPT-2 vs. ECMWF HRES and EPT-2e vs. ECMWF ENS — indicative RMSE/CRPS by lead-time band (arXiv:2507.09703) | Variable / Lead-time band | [EPT-2 RMSE vs. HRES](https://arxiv.org/html/2507.09703) | [EPT-2e CRPS vs. ENS mean](https://arxiv.org/html/2507.09703) | | --- | --- | --- | | 10 m wind, 0–48 h | EPT-2 lower RMSE than HRES | EPT-2e lower CRPS than ENS mean | | 10 m wind, 48–120 h | EPT-2 lower RMSE than HRES | EPT-2e lower CRPS than ENS mean | | 10 m wind, 120–240 h | EPT-2 lower RMSE than HRES | EPT-2e lower CRPS than ENS mean | | 100 m wind, 0–240 h | EPT-2 lower RMSE than HRES at every lead time | EPT-2e lower CRPS than ENS mean at virtually every lead time | | 2 m temperature, 0–240 h | EPT-2 lower RMSE than HRES at every lead time | EPT-2e lower CRPS than ENS mean at virtually every lead time | | Surface solar radiation, 0–240 h | EPT-2 lower RMSE than HRES at every lead time | EPT-2e lower CRPS than ENS mean at virtually every lead time |EPT-2 is benchmarked using open-source StationBench on more than 10,000 real ground stations, with no post-processing or station fine-tuning. This shared methodology makes the comparison auditable by any meteorologist. That accuracy comes at a fraction of traditional NWP cost. A single EPT-2 inference runs on a single GPU in minutes at approximately 0.25 kWh and $0.20–$15, which is roughly four orders of magnitude cheaper than an equivalent NWP simulation.
This cost advantage enables frequent, high-resolution updates. EPT2-HRRR runs at up to 5 km native resolution over Europe. EPT-2e updates 4 times per day, and EPT-2 RR updates up to 24 times per day, compared to the 2–4 daily runs available from ECMWF and GFS.
The P&L impact is direct. Jua’s forecasts carry an estimated $1.5 million profit and loss impact per gigawatt annually in European energy markets, translating to hundreds of millions for large portfolios. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately €1.5 M per year under typical hedging and imbalance-penalty structures. A 1 GW solar portfolio at the same accuracy gain saves approximately €3 M per year, and a 5 GW mixed renewables portfolio scales those economics linearly.
Jua for Energy keeps ECMWF in the stack and simplifies everything around it. ECMWF HRES and ENS remain in the customer’s workflow. EPT-2 and EPT-2e run alongside them on the same Jua platform workspace, with a unified schema and a single API, so the comparison stays live and the trader acts before the market does.
Frequently Asked Questions
Is the ECMWF model accurate enough for energy trading?
ECMWF HRES is the most accurate operational NWP model available and the universal benchmark for energy trading. Its accuracy is strongest in the 0–48 h window and degrades progressively beyond 120 h. For day-ahead power and gas positioning, HRES is reliable on temperature and synoptic wind.
For hub-height wind at 100 m, precipitation timing during convective events, and surface solar radiation at sub-daily resolution, HRES skill supports directional positioning but not precise generation dispatch. EPT-2 outperforms HRES on all four energy-critical variables across the full 0–240 h range, using the same open StationBench methodology described above, with no post-processing.
Is ECMWF better than GFS for energy trading?
ECMWF HRES consistently outperforms NOAA GFS on 2 m temperature, 10 m wind, 100 m wind, and precipitation across most lead-time bands. The gap is largest in the 48–120 h window, which governs day-ahead and multi-day energy contract pricing. GFS is a useful free baseline and a second opinion, but for a trading desk where forecast accuracy translates directly to P&L, ECMWF is the reference.
Both models are available on the Jua platform alongside EPT-2, EPT-2e, Microsoft Aurora, ECMWF AIFS, and 20+ additional models, under a unified schema.
Who has the most accurate hourly weather forecast in 2026?
EPT-2, the flagship model of Jua’s Earth Physics Transformer family, holds the global state of the art in atmospheric prediction as of 2026. It outperforms ECMWF HRES, Microsoft Aurora, Google DeepMind GraphCast, NOAA GFS, and ECMWF AIFS on 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation across the full 0–240 h lead-time range, evaluated using the same open-source StationBench methodology described above, with no post-processing.
EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Both results are documented in the peer-reviewed technical report arXiv:2507.09703.
How does EPT-2 differ from ECMWF’s own AI model (AIFS)?
ECMWF AIFS is ECMWF’s AI-based forecasting system, available on the Jua platform alongside HRES and ENS. EPT-2 outperforms AIFS on the energy-critical variables benchmarked in arXiv:2507.09703. The architectural difference is also meaningful. EPT-2 produces forecasts at arbitrary lead times (native any-Δt), while most AI peers including AIFS roll forward in fixed time steps, which compounds error at extended lead times.
EPT-2 does not roll. EPT-2 RR also updates up to 24 times per day, compared to the 4 daily cycles available from ECMWF’s operational systems.
What is the financial cost of a forecast miss for a wind portfolio?
As detailed earlier, a 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately €1.5 M per year, while a 1 GW solar portfolio at the same accuracy gain saves approximately €3 M per year. For multi-GW portfolios, common among regulated utilities and large trading houses, these economics scale linearly.
The cost of a single missed wind ramp or unforecast cold snap can exceed the annual cost of a premium forecast subscription. Customers including Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Québec run Jua for Energy alongside their existing ECMWF subscriptions to capture this margin.
Conclusion: Turning Accuracy into Tradeable Edge
Three criteria determine whether a forecast is tradeable: accuracy by variable and lead time, update frequency between NWP cycles, and usability inside the actual trading workflow. ECMWF HRES leads NWP on accuracy and remains the reference every serious desk keeps. It cannot address the second or third criteria, because four runs per day form a hard compute ceiling and raw grib files do not match how traders work.
EPT-2 outperforms ECMWF HRES on every lead time and on every energy-critical variable: 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation. EPT-2e beats the 50-member ECMWF ENS mean on RMSE and CRPS at virtually every lead time. EPT-2 RR updates up to 24 times per day.
The Jua platform brings all 25+ models, including ECMWF HRES, ENS, AIFS, GFS, Aurora, GraphCast, and the full EPT family, into a single workspace with one schema and one API. Traders see live benchmarking, automatic briefings, and divergence alerts in the same place where they already work. Athena, the AI agent in this workspace, turns a natural-language question into a briefing, a benchmark, or a backtest in about 90 seconds, so desks can act on superior EPT-2 accuracy without changing their workflow.
Jua is a foundation model and agent company, and Jua for Energy is the first applied product built on this architecture. The atmosphere is the first physical system EPT has been fine-tuned for, and energy trading is the first market where Athena is instrumented. The numbers speak, and a live benchmark on your own region and variable takes less than 5 minutes to run.