Global Coverage Weather API: Jua’s EPT-2 Beats ECMWF

Written by: Olivier Lam, Physical AI Team, Jua.ai AG | Last updated: July 3, 2026

Key Takeaways

• A global weather API for energy trading must deliver accurate deterministic and probabilistic forecasts at a cadence that matches trading decisions, or imbalance costs and mispriced positions follow.
• Jua for Energy’s EPT-2 model outperforms ECMWF HRES across all key energy variables and lead times, while EPT-2e beats the 50-member ECMWF ENS mean on RMSE and CRPS.
• EPT-2e updates four times daily and the rapid-refresh variant (EPT2-RR) updates up to 24 times per day, far beyond the two to four daily runs typical of traditional NWP providers.
• Jua for Energy supplies years of hindcast data in the same schema as live forecasts, so quants can backtest weather-signal strategies without building separate data pipelines.
• Book a demo with Jua to benchmark EPT-2 head-to-head against your current provider in under five minutes: schedule here.

Accuracy Leaderboard for Global Weather APIs

The table below reveals a clear accuracy hierarchy for energy trading. One provider surpasses the ECMWF benchmark across all energy-relevant variables and lead times, while consumer-grade APIs miss critical variables such as hub-height wind and SSRD. Every accuracy claim is anchored to arXiv 2507.09703. Meteomatics appears as a representative data-vendor category; OpenWeatherMap and Visual Crossing represent consumer-grade providers.

Hub-height wind (100 m) and surface solar radiation downwelling (SSRD) do not appear in the published output sets of OpenWeatherMap, Visual Crossing, or Aurora. Energy traders rely on those variables for wind-turbine and solar-generation forecasting. Hindcast availability and SDK ergonomics appear in later sections.

Book a demo to see EPT-2 head-to-head against your current forecast provider.

Most Accurate Weather API for Energy Trading

EPT-2 outperforms ECMWF HRES on every lead time from 0 to 240 hours across 10 m wind speed, 100 m wind speed, 2 m temperature, and surface solar radiation. The evaluation uses open-source StationBench, benchmarked against more than 10,000 real ground stations with no post-processing or station fine-tuning. This standard removes the overfitting risk that often inflates vendor-reported accuracy figures.

On deterministic skill, EPT-2 beats Microsoft Aurora on 10 m wind, 100 m wind, and 2 m temperature across the full 0–240 hour range. Aurora publishes no SSRD output, so EPT-2 holds the advantage on that variable by default. The gap is material: EPT-2 outperforms leading AI weather models and traditional numerical baselines across all forecast horizons on RMSE.

On probabilistic skill, EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. EPT-2e reaches this ensemble advantage with 10 published members against the ENS 50, which reflects both forecast sharpness and reliability. No other AI weather provider ships a productised ensemble that clears this bar.

Consumer-grade providers such as OpenWeatherMap and Visual Crossing blend NWP outputs without publishing head-to-head benchmarks against ECMWF HRES on energy-relevant variables. Meteomatics resells processed NWP and passes through third-party ensemble outputs without a proprietary model benchmark. Neither category provides a suitable accuracy foundation for a systematic energy-trading strategy.

Global Weather API Update Frequency Comparison

Accuracy alone does not help if the forecast is stale when a trader acts on it. Update frequency determines how stale a forecast is at execution time. Traditional numerical weather prediction (NWP), the method used by ECMWF and NOAA, runs on high-performance computing clusters that consume approximately 8,400 kWh per simulation at a cost of €1,000–€20,000. That compute ceiling caps global NWP at two to four runs per day. Between runs, every provider that resells or wraps NWP outputs inherits the same staleness.

EPT-2e updates four times per day. EPT2-RR, Jua’s rapid-refresh variant, updates up to 24 times per day. This cadence is possible because a single EPT-2 inference runs on a single GPU in minutes at approximately $0.20–$15 and ~0.25 kWh, roughly four orders of magnitude cheaper than an equivalent NWP simulation. Jua’s models natively forecast at up to 5 km spatial resolution over Europe (EPT2-HRRR).

Meteomatics offers hourly updates by passing through third-party model outputs. OpenWeatherMap updates every 10 minutes to 1 hour on paid tiers, while Visual Crossing offers multiple updates per day on paid tiers. Microsoft Aurora operates on a research cadence without a published operational refresh schedule. ECMWF HRES runs 2–4 times per day.

For intraday energy trading, where a wind ramp or solar dip can move the spread before the next NWP run lands, the difference between 4 and 24 daily updates becomes a measurable P&L variable.

Historical Forecast Data for Backtesting

Hindcast depth, meaning the length and consistency of historical forecast data, separates production-grade APIs from research tools. A quant developer building a systematic weather-signal strategy needs years of forecast data in the same schema as the live feed. Reanalysis alone, which represents what the atmosphere did rather than what a model predicted, cannot support robust evaluation of forecast-based alpha.

Jua for Energy provides hindcast data across multiple EPT-family and third-party models through a unified schema, accessible via the REST API and Python SDK. ERA5, ECMWF’s reanalysis product available from 1990 onward at 0.25° resolution and hourly cadence, is integrated as the historical reference baseline and was part of the training data for the EPT family. Backtests against years of historical forecasts run quickly via Athena.

ECMWF HRES hindcasts are available to paying members via the MARS archive. OpenWeatherMap and Visual Crossing offer historical weather observation data but not historical forecast data, which traders need for forecast-based backtests rather than observation-based ones. Meteomatics provides access to historical model data on paid tiers. Microsoft Aurora and Google DeepMind GraphCast do not ship productised hindcast access, so quant teams using those outputs must build their own archiving infrastructure.

Book a demo to run a backtest on your own region and variables against 25+ models.

REST API and Python SDK for Trading Integration

Jua for Energy exposes more than 25 models, including 10 proprietary EPT models and 15 third-party NWP and AI models such as ECMWF HRES, ECMWF ENS, ECMWF AIFS, NOAA GFS, DWD ICON, Microsoft Aurora, and GFS GraphCast, through a single REST API (POST /v1/forecast/data and related endpoints) with Apache Arrow support for large columnar payloads. The Python SDK installs via pip install jua from PyPI and provides forecast access, hindcast and backtesting, and weather-parameter standardisation across all models in a unified schema.

Apache Arrow support matters for quant teams running continental, multi-variable, multi-model backtests, because JSON-based APIs struggle at those data volumes. A unified schema across more than 25 models means swapping or comparing models does not require re-engineering the ingestion pipeline. Documentation lives at docs.jua.ai, the developer dashboard at developer.jua.ai, and the query engine at query.jua.ai/docs.

OpenWeatherMap and Visual Crossing offer REST APIs with JSON payloads and no Apache Arrow support. Meteomatics provides a REST API with proprietary query syntax. ECMWF distributes data as grib files via the MARS archive, which requires a separate processing pipeline before use in a Python-based trading stack. Aurora and GraphCast are available as research code with limited or no productised API access.

Natural-Language Analyst Layer: Athena

Athena, Jua’s AI agent, sits on top of the Jua for Energy tool surface. A trader or quant developer types a natural-language request such as “what is the 100 m wind forecast spread across models for northern Germany tonight?” or “backtest a wind-ramp strategy on EPT-2e over the last two winters.” Athena plans the workflow, calls tools, evaluates intermediate outputs, and returns the answer, the underlying widget, or the full backtest report. Athena turns raw physics predictions from EPT-2 into actionable analysis by reading market context and modeling participant behavior. Typical queries resolve in approximately 90 seconds, and backtests complete in minutes.

No other global weather API provider, whether NWP incumbent, AI peer, or data vendor, ships a natural-language analyst layer with this capability. Aurora and GraphCast remain research outputs without an agent layer. ECMWF’s product surface focuses on member-facing data access, not workflow tooling. Point-solution SaaS vendors and meteorology consultancies deliver reports on a fixed schedule, not on demand in 90 seconds.

Run benchmarks on your own region and variables on the Jua platform. See your forecasts in less than 5 minutes, head-to-head against 25+ models, at athena.jua.ai.

Free Tier and Enterprise Access Options

OpenWeatherMap and Visual Crossing offer free tiers with rate-limited API calls and no ensemble, hub-height wind, or SSRD access. Those tiers work for prototyping consumer applications but not for production energy-trading pipelines. Meteomatics offers a trial API with restricted call volumes. ECMWF data is available to member states and registered research institutions, while commercial access requires a separate agreement. Microsoft Aurora and GraphCast are available as research outputs under academic licensing.

Jua for Energy operates as an enterprise product with authenticated API and SDK access under agreed terms. A live benchmark proof-of-value runs on the prospect’s own region and variable at athena.jua.ai before any commercial conversation.

API Selection Readiness Checklist

Before committing to a weather API provider, validate these six technical requirements in your own environment. Each one closes a specific capability gap that otherwise appears later in production.

• Start by verifying deterministic accuracy on your own region and variable against ECMWF HRES using an independent benchmark methodology such as StationBench or equivalent ground-station validation, which establishes whether the provider’s base forecast is reliable.
• Once base accuracy is confirmed, evaluate ensemble skill on RMSE and CRPS at the lead times that match your trade horizon, whether day-ahead, intraday, or multi-day, because position sizing depends on calibrated uncertainty estimates rather than point forecasts alone.
• With forecast quality established, confirm hindcast depth, ensuring that historical forecast data, not just reanalysis, is available in the same schema as the live feed so you can backtest whether the verified forecast edge would have generated alpha historically.
• Next, test SDK latency and payload format under production data volumes, and require Apache Arrow support for continental multi-model queries to avoid bottlenecks from JSON payloads.
• Then evaluate update cadence against your intraday trade windows, keeping in mind that 4×/day represents the NWP ceiling while 24×/day reflects the EPT2-RR ceiling for rapid refresh.
• Finally, confirm hub-height wind (100 m) and SSRD coverage, because both variables are missing from consumer-grade providers and Aurora yet remain essential for wind and solar strategies.

Conclusion: Six Bars for Production-Grade Weather APIs

A global-coverage weather API for production energy trading must clear six bars at once: deterministic accuracy above ECMWF HRES, ensemble skill above the ECMWF ENS mean, update frequency above the NWP ceiling, hindcast depth sufficient for multi-year backtesting, hub-height wind and SSRD coverage, and a developer stack that stands up in days rather than quarters. EPT-2 clears every one of those bars, and Jua for Energy, built on EPT and the Athena agent, is the only platform that combines all six with a natural-language analyst layer and a 25-model benchmarking surface. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves roughly €1.5 M per year under typical hedging and penalty structures, and multi-GW portfolios scale those economics linearly.

Run benchmarks on your own region and variables on the Jua platform. See your forecasts in less than 5 minutes, head-to-head against 25+ models, at athena.jua.ai. Or book a demo to see EPT-2 head-to-head against your current provider.

Frequently Asked Questions

What variables does a production-grade global weather API need to cover for energy trading?

Energy trading depends on a minimum variable set that consumer-grade APIs usually miss. Wind speed at hub height, typically 100 m above ground though turbine hub heights range from 80 m to 160 m, drives wind-generation forecasts. Surface solar radiation downwelling (SSRD) drives solar-generation forecasts. Two-meter temperature drives heating and cooling load. Precipitation and cloud cover affect hydro dispatch and solar output.

A production API must expose these variables at the spatial resolution and update cadence that match the trade horizon. Intraday positions require sub-hourly refresh and high spatial resolution. Day-ahead and multi-day positions require ensemble outputs with calibrated probabilistic skill.

Jua for Energy covers 25 variables including wind at 11 height levels from 10 m to 200 m, SSRD, precipitation, cloud cover, temperature at multiple levels, and pressure, all accessible through a single unified schema.

What is the difference between a hindcast and a reanalysis, and why does it matter for backtesting?

A reanalysis, with ERA5 as the standard reference, represents the best retrospective estimate of what the atmosphere actually did, produced by running a fixed model version over historical observations. A hindcast is a historical forecast that records what a specific model predicted at a specific initialization time for a specific lead time in the past.

For backtesting a forecast-based trading strategy, hindcasts provide the correct dataset. A strategy that trades on the day-ahead wind forecast needs to know what the day-ahead wind forecast said at 06:00 UTC on each historical day, not what the atmosphere actually did. Using reanalysis as a proxy for historical forecasts systematically overstates strategy performance, because reanalysis has access to observations that were not available at forecast initialization time.

Jua for Energy provides hindcast data across multiple EPT-family and third-party models in the same schema as the live feed, which enables apples-to-apples backtesting without a separate data-engineering project.

How does EPT-2 differ architecturally from models like Microsoft Aurora or ECMWF AIFS?

EPT, the Earth Physics Transformer, is a general spatiotemporal transformer foundation model trained to learn the governing physics of complex systems directly from observational data. Its outputs respect conservation laws such as mass, momentum, and energy, which are encoded in the latent representation rather than imposed as post-processing corrections. EPT-2 produces forecasts at native any-Δt, because it is trained to predict at arbitrary lead times rather than rolling forward in fixed increments.

Aurora and most peer models train on a fixed 6-hour grid and roll forward in 6-hour steps, which compounds error at longer lead times. ECMWF AIFS is an AI model built on top of ECMWF’s own data infrastructure, and it runs on the Jua for Energy platform as one of 15 third-party models.

The architectural difference has a practical consequence. EPT-2 outperforms Aurora on 10 m wind, 100 m wind, and 2 m temperature across the full 0–240 hour range, and Aurora has no SSRD output at all.

Can Jua for Energy integrate with existing internal trading and risk systems?

Jua for Energy integrates cleanly with existing trading and risk systems. It exposes a REST API with Apache Arrow payload support and a Python SDK installable via pip install jua. The API uses a unified schema across all 25+ models on the platform, so integrating a new model or switching between models does not require re-engineering the ingestion pipeline.

Hindcast data is available for backtesting in the same schema as the live feed. ENTSO-E grid data, including actual generation, capacity, and PSR classifications across European power markets, is integrated directly. Quant developers at funds and trading houses pipe Jua forecasts into their own systematic models, while utilities and trading houses pipe them into existing dispatch, risk, and trading tools. The integration that takes a quarter to build from raw research-model outputs typically stands up in days on the Jua for Energy stack.

How does Jua for Energy handle model uncertainty, and why does ensemble skill matter for trading?

Ensemble forecasting runs multiple model initializations with perturbed initial conditions to produce a probability distribution over future atmospheric states rather than a single deterministic trajectory. For energy trading, ensemble skill, measured by RMSE on the ensemble mean and CRPS on the full distribution, determines how reliably a trader can size a position around forecast uncertainty.

A sharp, well-calibrated ensemble allows a trader to distinguish between a high-confidence forecast, where the spread is narrow and conviction is appropriate, and a low-confidence forecast, where the spread is wide and hedging or reduced exposure makes sense. EPT-2e, Jua’s ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time. No other AI weather provider ships a productised ensemble that clears this bar.

The ensemble output is accessible through the same REST API and Python SDK as the deterministic EPT-2 forecast, with no separate integration required.