Written by: Olivier Lam, Physical AI Team, Jua.ai AG
Key Takeaways for European Energy Desks
- Forecast accuracy in European energy markets is measured by RMSE and CRPS against real ground stations. Even small gains translate directly into millions in hedging and imbalance savings.
- EPT-2, Jua’s deterministic flagship model, outperforms ECMWF HRES on every lead time from 0–240 hours across four energy-critical variables: 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation.
- EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Rapid-refresh variants update up to 24 times per day.
- The Jua platform benchmarks more than 25 models simultaneously, provides live head-to-head comparisons, divergence alerts, and 5-minute backtests through a single REST API and Python SDK.
- Compare EPT-2 with your current forecast provider in a tailored demo with Jua.
Executive Summary and Evaluation Lens for Traders
This guide benchmarks the leading weather forecasting systems available to European energy professionals in 2026. The evaluation framework uses RMSE and CRPS against more than 10,000 real ground stations via the open-source StationBench methodology, with no post-processing and no station fine-tuning. The primary variables are the four that drive European energy P&L: 10 m wind speed, 100 m wind speed, 2 m temperature, and surface solar radiation downwelling (SSRD). Lead times span 0–240 hours.
The headline result is clear. EPT-2, the deterministic flagship model inside Jua for Energy, outperforms ECMWF HRES on every lead time and on all four energy-critical variables. These findings are documented in peer-reviewed technical reports on arXiv:2507.09703 and arXiv:2410.15076. EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Jua operates as a horizontal foundation-model and agent platform with Jua for Energy as its flagship vertical product. The sections below show how these benchmark gains convert into concrete trading advantages for European energy desks.
Compare EPT-2’s performance against your current provider in a live demo.
Model Landscape: 25 Forecast Systems on One Platform
The Jua platform benchmarks more than 25 models simultaneously, including 10 proprietary AI models from the EPT family and 15 third-party NWP and AI models. The table below covers the primary systems relevant to European energy professionals, with spatial resolution and refresh cadence as the key operational axes. ECMWF IFS HRES operates at native 9 km spatial resolution on the O1280 grid, with four runs per day at 00, 06, 12, and 18 UTC, and each run provides a 15-day forecast horizon.
| Model | Type | Native Spatial Resolution | Update Frequency |
|---|---|---|---|
| EPT-2 (Jua) | AI foundation model, deterministic | Up to 5 km (Europe) | 4×/day |
| EPT-2e (Jua) | AI foundation model, ensemble | Up to 5 km (Europe) | 4×/day |
| EPT-2 RR (Jua) | AI foundation model, rapid refresh | Up to 5 km (Europe) | Up to 24×/day |
| EPT-2 HRRR (Jua) | AI foundation model, high-res rapid refresh | ~5 km (Europe) | Up to 24×/day |
| EPT-2 Early (Jua) | AI foundation model, early dissemination | Up to 5 km (Europe) | 4×/day |
| EPT-2 Reasoning (Jua) | AI foundation model, blended reasoning | Up to 5 km (Europe) | 4×/day |
| EPT-1.5 (Jua) | AI foundation model, previous generation | Up to 5 km (Europe) | 4×/day |
| ECMWF IFS HRES | NWP, deterministic | 9 km (global) | 4×/day |
| ECMWF IFS ENS | NWP, 50-member ensemble | 18 km (global) | 4×/day |
| ECMWF AIFS | AI, ECMWF proprietary | ~25 km (global) | 4×/day |
| ECMWF EC46 | NWP, extended range | 36 km (global) | 2×/day |
| NOAA GFS | NWP, deterministic | 13 km (global) | 4×/day |
| NOAA GFS Ensemble Mean | NWP, ensemble mean | 25 km (global) | 4×/day |
| Microsoft Aurora | AI, research output | ~25 km (global) | Typically 4×/day |
| GFS GraphCast (DeepMind) | AI, research output | ~25 km (global) | Typically 4×/day |
| DWD ICON Global | NWP, deterministic | 13 km (global) | 4×/day |
| DWD ICON-EU | NWP, regional | 6.5 km (Europe) | 8×/day |
| KNMI HARMONIE-AROME | NWP, regional | ~2.5 km (NW Europe) | 8×/day |
| UKMO (Met Office) | NWP, regional | ~2 km (UK) | 4×/day |
| ICON-D2 (DWD) | NWP, convection-permitting | ~2 km (Germany) | 8×/day |
Note: Jua for Energy product resolution reaches up to 1 km when comparing platform outputs. EPT-2 RR and EPT-2 HRRR update up to 24 times per day, and all other EPT variants update 4×/day. Third-party model resolutions and cadences reflect published operational specifications as of June 2026.
Core Forecast Metrics and StationBench Methodology
RMSE (Root Mean Square Error) measures the average magnitude of forecast error against observed values, and lower values indicate higher accuracy. CRPS (Continuous Ranked Probability Score) evaluates the full probability distribution of an ensemble forecast against a single observation. Lower CRPS is better and penalises both overconfident and underconfident ensembles.
NWP (Numerical Weather Prediction) solves differential equations on a three-dimensional atmospheric grid and has served as the operational standard for forty years. An ensemble runs multiple perturbed model instances to produce a probability distribution rather than a single deterministic output. Lead time is the number of hours between forecast issuance and the valid time being predicted. A hindcast applies a model to historical periods to evaluate skill against known outcomes and supports backtesting of trading strategies.
EPT-2 is benchmarked using StationBench, an open-source evaluation framework that measures model outputs against more than 10,000 real ground-station observations across Europe and globally, with no post-processing or station fine-tuning applied. This methodology matches the approach used in arXiv:2507.09703. Under this framework, EPT-2 maintains a consistent performance edge over ECMWF HRES across the full 0–240 hour window on the four key variables. EPT-2 delivers hourly global weather updates and outperforms leading AI weather models and traditional numerical baselines across all forecast horizons on RMSE.
Test these benchmarks yourself and run your first comparison in under 5 minutes at athena.jua.ai.
Performance on European Energy Variables
The table below focuses on the four variables that determine European energy P&L and compares EPT-2 and EPT-2e against primary benchmarks across the 0–240 hour lead-time window. All results come from StationBench evaluation against European ground stations, as documented in arXiv:2507.09703.
| Variable | EPT-2 vs ECMWF HRES (RMSE, 0–240 h) | EPT-2e vs ECMWF ENS Mean (CRPS) | EPT-2 vs Microsoft Aurora (RMSE) |
|---|---|---|---|
| 10 m wind speed | EPT-2 wins on every lead time | EPT-2e wins at virtually every lead time | EPT-2 wins across full 0–240 h range |
| 100 m wind speed | EPT-2 wins on every lead time | EPT-2e wins at virtually every lead time | EPT-2 wins across full 0–240 h range |
| 2 m temperature | EPT-2 wins on every lead time | EPT-2e wins at virtually every lead time | EPT-2 wins across full 0–240 h range |
| Surface solar radiation (SSRD) | EPT-2 wins on every lead time | EPT-2e wins at virtually every lead time | EPT-2 wins by default, as Aurora has no SSRD output |
Source: arXiv:2507.09703. All comparisons use StationBench against European ground stations. EPT-2e uses 10 members as published in the technical report.
Two operational pain points define how European energy traders experience forecast data. The first is stale runs. ECMWF IFS global model runs require 4–6 hours of computation after initialisation before results are distributed, so the 00 UTC run may not reach a trading desk until mid-morning. EPT-2 RR updates up to 24 times per day, and a typical Jua run completes about 2.5 hours ahead of competing operational runs at the same cycle.
The second pain point is silent revisions. When ECMWF or GFS revises an output mid-cycle, the revision often arrives without notification. Divergence alerts on the Jua platform fire the moment two models disagree on a key variable, and correction alerts fire the moment a model revises its own prior output. Traders see the trade window open through an alert instead of discovering a missed move after the fact.
Integrating Jua for Energy into Daily Operations
Jua for Energy runs alongside an existing ECMWF subscription rather than replacing it. ECMWF AIFS, ECMWF’s own AI model, runs natively on the Jua platform under the same unified schema as EPT-2, NOAA GFS, DWD ICON, Microsoft Aurora, and GFS GraphCast. The integration point is a single REST API endpoint (POST /v1/forecast/data) with Apache Arrow support for large payloads, or the Python SDK installed via pip install jua.
The 7–9 a.m. manual prep routine compresses into a single workspace. Teams no longer download grib files, push them through in-house pipelines, wait for the meteorologist’s briefing, and stitch spreadsheets and terminal screens. Day-Ahead and Intraday briefings auto-refresh on every new model run and cover model consensus across more than 25 models, model delta since the prior run, convergence tracking, and price implications.
Athena, Jua’s AI agent instrumented with the Jua for Energy tool surface, answers follow-up questions in natural language. Typical queries resolve in about 90 seconds, and backtests complete in about 5 minutes. Jua serves major utilities across four continents, including some of Europe’s largest energy companies, as well as commodity traders and hedge funds, such as Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Québec.
Readiness and Opportunity Assessment for Portfolios
Forecast accuracy improvements translate directly into portfolio-level economics. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately €1.5 M per year. These portfolio-level savings, about €1.5 M for wind and €3 M for solar at 1 GW scale, assume typical hedging and imbalance penalty structures and scale linearly for multi-GW portfolios.
The infrastructure cost asymmetry strengthens the case. A single EPT-2 inference uses approximately 0.25 kWh and costs between $0.20 and $15 on a single GPU, completing in minutes. A single traditional NWP simulation consumes about 8,400 kWh and costs €1,000–€20,000 on HPC infrastructure, with 1–2 hours of compute time. EPT-2 was trained on 8 × H100 GPUs over 10 days, while Microsoft Aurora required 32 × A100 GPUs over 18 days. The cost gap at inference reaches roughly four orders of magnitude and makes 24 daily updates economically viable, while traditional NWP typically remains capped at two to four.
Common Pitfalls When Adopting AI Weather Models
Stale forecasts between runs. Relying solely on 4×/day NWP outputs leaves a trading desk working with numbers that can be up to six hours old before distribution latency is added. EPT-2 RR and EPT-2 HRRR update up to 24 times per day, and actual-generation power forecasts on the Jua platform refresh every 15 minutes.
Silent model revisions. Traditional NWP systems often revise outputs without notification. Correction alerts on the Jua platform fire automatically the moment a model revises its own prior output, and divergence alerts fire when two models disagree. These alerts surface the trade window before the market fully re-prices.
Lack of hindcast access for backtesting. Quant teams that subscribe to AI weather research outputs often receive raw model files without hindcast data and must build ingestion pipelines and backtesting harnesses from scratch. The Jua platform exposes hindcast data across multiple Jua and third-party models through the same API schema, and backtests run in about 5 minutes via Athena or programmatically through the Python SDK.
Evaluating AI models on vendor-provided graphics. Meteorologists who rely on marketing graphics instead of running head-to-head benchmarks face selection bias. The Jua platform’s live benchmarking surface returns a head-to-head RMSE and CRPS comparison on any region and variable in seconds, using the same StationBench methodology detailed earlier.
FAQ
What is the best weather forecast model for Europe?
Measured by RMSE and CRPS against European ground stations across the 0–240 hour lead-time window, EPT-2 currently leads on the four variables that drive European energy P&L: 10 m wind speed, 100 m wind speed, 2 m temperature, and surface solar radiation. EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. ECMWF HRES remains the universal benchmark after forty years of NWP leadership, and serious energy professionals keep it in their stack. EPT-2 extends that stack by providing a documented performance advantage on the variables and lead times that matter most to European energy trading, using the same peer-reviewed methodology referenced earlier and evaluated against more than 10,000 real ground stations with no post-processing.
Is GFS or Euro (ECMWF) more accurate for European weather?
For European energy variables such as wind at hub height, near-surface temperature, and solar radiation, ECMWF HRES consistently outperforms NOAA GFS on RMSE at most lead times. This performance is why ECMWF serves as the reference model for European energy trading. Both ECMWF HRES and NOAA GFS are available on the Jua platform under a unified schema, which allows direct head-to-head comparison on any region and variable. EPT-2 sits above both and maintains its advantage across the four primary European energy variables over the full 0–240 hour range, while EPT-2 RR updates up to 24 times per day versus the 4×/day cadence shared by ECMWF HRES and GFS.
How does EPT-2 compare to ECMWF HRES on European energy variables?
EPT-2 maintains a consistent edge over ECMWF HRES across the full 0–240 hour window on 10 m wind speed, 100 m wind speed, 2 m temperature, and surface solar radiation downwelling. These results rely on the same StationBench methodology described in the benchmark section, with more than 10,000 real ground stations and no post-processing or station fine-tuning. EPT-2e, the ensemble variant with 10 members as published in arXiv:2507.09703, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. Jua for Energy runs alongside ECMWF and replaces the manual plumbing around it, including the grib pipeline, spreadsheet stitching, morning briefing routine, and bespoke benchmarking harness.
How quickly can I evaluate EPT-2 against my current forecast provider?
The live benchmark on the Jua platform returns a head-to-head RMSE and CRPS comparison on any region and variable in seconds from first selection. A full backtest against years of historical forecasts runs in about 5 minutes via Athena. The Python SDK installs via pip install jua and exposes more than 25 models through a single schema with Apache Arrow support, so quant teams can run their own programmatic backtests without building a separate ingestion pipeline. Many meteorologists who start as sceptics become internal champions once they see their own benchmark results.
Can Jua for Energy integrate with existing ECMWF pipelines and internal trading systems?
Jua for Energy exposes a REST API with Apache Arrow payload support and a Python SDK on PyPI. ECMWF HRES, ENS, AIFS, NOAA GFS, DWD ICON, Microsoft Aurora, and GFS GraphCast all run on the Jua platform under a unified schema, so swapping or comparing models does not require re-engineering existing pipelines. ENTSO-E grid data integrates directly for European power-market data, including actual generation and PSR classifications. Hindcast data is available across multiple Jua and third-party models for backtesting. Quant teams pipe Jua forecasts directly into systematic trading models, and utilities and trading houses feed them into dispatch, risk, and trading tools. Integration work that often takes a quarter elsewhere typically completes within days.
Conclusion and Next Steps for Energy Teams
The most accurate weather forecast for European energy applications depends on RMSE and CRPS measured against real ground stations at the lead times and on the variables that determine P&L. Under that lens, EPT-2 holds a documented advantage over ECMWF HRES across the 0–240 hour window on 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation, and EPT-2e maintains a similar edge over the 50-member ECMWF ENS mean on RMSE and CRPS. A 1 GW wind portfolio that captures four percentage points of accuracy improvement saves about €1.5 M per year, and a 1 GW solar portfolio saves about €3 M per year.
Jua operates as a foundation model and agent company, with Jua for Energy as its first applied product in the same way Anthropic relates to Claude Code. EPT, the Earth Physics Transformer, is a general spatiotemporal foundation model that learns the governing physics of complex systems from observational data. Athena is an AI agent that turns natural-language objectives into briefings, benchmarks, backtests, and widgets in about 90 seconds. The atmosphere is the first physical system EPT has been fine-tuned for, and energy trading is the first market Athena has been instrumented for, with further domains on the roadmap.
Quant developers and engineering teams can start by installing the SDK with pip install jua or by reading the API documentation at docs.jua.ai. From there, they can wire forecasts directly into existing models and tools.
Schedule a Jua for Energy session to see EPT-2 on your regions, assets, and trading horizon.