{"id":307,"date":"2026-05-08T23:18:39","date_gmt":"2026-05-08T23:18:39","guid":{"rendered":"https:\/\/jua.ai\/articles\/ai-weather-forecast-accuracy\/"},"modified":"2026-07-04T05:04:49","modified_gmt":"2026-07-04T05:04:49","slug":"ai-weather-forecast-accuracy","status":"publish","type":"post","link":"https:\/\/jua.ai\/articles\/ai-weather-forecast-accuracy\/","title":{"rendered":"AI Weather Forecast Accuracy Update: EPT-2 Beats ECMWF"},"content":{"rendered":"<p><em>Written by: Olivier Lam, Physical AI Team, Jua.ai AG | Last updated: June 28, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Energy Traders<\/h2>\n<ul>\n<li>EPT-2 outperforms ECMWF HRES on every variable and lead time from 0\u2013240 hours for 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation.<\/li>\n<li>EPT-2e, the ensemble variant, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time.<\/li>\n<li>Physics-constrained architecture and native any-\u0394t forecasting prevent error accumulation and keep forecasts physically consistent across all ranges.<\/li>\n<li>Inference runs in minutes on a single GPU at ~0.25 kWh, so traders can access up to 24 updates per day instead of traditional NWP limits.<\/li>\n<li><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Benchmark EPT-2 on your own data<\/strong><\/a> and see it head-to-head against your current forecast provider.<\/li>\n<\/ul>\n<h2>The Problem: Stale Weather Data in Fast Energy Markets<\/h2>\n<p>The energy industry effectively runs on two supercomputers. ECMWF and NOAA each produce global numerical weather prediction (NWP) runs that the entire sector uses to price renewable output, heating demand, and system tightness. Even with supplementary runs, <a href=\"https:\/\/www.bloomberg.com\/news\/articles\/2026-03-26\/energy-traders-turn-to-ai-to-forecast-the-weather-forecast?embedded-checkout=true\" target=\"_blank\">the ECMWF two-week outlook remains the definitive reference point for traders repricing risk<\/a>, and it updates only two to four times per day. Between runs, traders hold stale numbers while markets move.<\/p>\n<p>The AI weather model market has responded with a wave of claims that are difficult to verify. Meteorologists and quant developers evaluating these claims need peer-reviewed RMSE and CRPS tables across lead times and variables, not vendor graphics, to separate performance from marketing. The EPT-2 technical report addresses that verification gap directly. It provides peer-reviewed accuracy metrics evaluated against more than 10,000 real ground stations on open-source StationBench, with no post-processing or station fine-tuning.<\/p>\n<h2>Core Risks of AI Weather Models<\/h2>\n<p>AI weather models face three legitimate objections. First, physics consistency matters. A standard transformer applied naively to atmospheric data can produce outputs that violate conservation laws for mass, momentum, and energy. Those forecasts may look plausible yet remain physically impossible.<\/p>\n<p>Second, localized extremes often cause trouble. Models trained on gridded reanalysis data may underresolve phenomena that occur at sub-grid scale, including orographic wind acceleration and convective initiation. These are exactly the conditions that drive large P&amp;L swings.<\/p>\n<p>Third, long-range degradation affects many architectures. Autoregressive models that roll forward in fixed time steps compound error with each step. Skill at 10-day lead times then depends heavily on whether the architecture accumulates that error or suppresses it.<\/p>\n<p>EPT-2 addresses all three risks directly. It is a spatiotemporal transformer foundation model trained on observational physics, so conservation laws that constrain mass, momentum, and energy are learned at the representation level rather than imposed as a post-processing correction. EPT-2 also forecasts at native any-\u0394t. It is trained to predict at arbitrary lead times instead of rolling forward in fixed 6-hour increments. Microsoft Aurora and most peer models roll forward in 6-hour steps and compound error, while EPT-2 does not roll. The accuracy advantage documented in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a> therefore holds across the full 0\u2013240 hour range, not just at short lead times where all models perform well.<\/p>\n<h2>Performance on Extreme Events That Move P&amp;L<\/h2>\n<p>Extreme events expose unconstrained AI models. The training distribution may not adequately represent tail events, and without physics constraints, the model has no mechanism to recover physical plausibility when it encounters conditions outside that distribution. A model that violates energy conservation during a heatwave or a wind ramp is not a model a trader can rely on.<\/p>\n<p>EPT-2&#8217;s physics grounding provides a structural constraint that generic transformers lack. Conservation laws are learned directly from observational data in a latent representation that integrates forward in time. The evaluation methodology in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a> uses more than 10,000 real ground stations with no post-processing. The benchmark therefore captures performance on actual observed conditions, including the tail events that matter most to energy P&amp;L.<\/p>\n<p>EPT-1.5, the previous-generation model documented in <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2410.15076<\/a>, already outperformed GraphCast, FuXi, Pangu-Weather, and ECMWF HRES on European wind and temperature. EPT-2 extends that lead across all four primary energy variables. That deterministic performance matters for point forecasts, and extreme-event risk management then builds on probabilistic output that captures uncertainty across the full distribution of possible outcomes.<\/p>\n<h2>Ensemble Skill Versus ECMWF ENS for Risk Management<\/h2>\n<p>Probabilistic forecasting, meaning ensemble output that quantifies forecast uncertainty rather than a single deterministic trace, is the standard for serious energy risk management. ECMWF ENS, the 50-member operational ensemble, has been the gold standard for probabilistic NWP for decades.<\/p>\n<p>EPT-2e, the ensemble variant of EPT-2, beats the 50-member ECMWF ENS mean on both RMSE and CRPS at virtually every lead time, as documented in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. CRPS measures the full probabilistic skill of a forecast. It penalizes both bias and spread miscalibration, which makes it the appropriate metric for ensemble comparison. No AI weather peer has shipped a productised ensemble equivalent.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Run a live EPT-2e vs. ECMWF ENS comparison<\/strong><\/a> on your own region and variable.<\/p>\n<h2>Operational Cost and Speed Advantages for Intraday Trading<\/h2>\n<p>Traditional NWP carries a heavy compute bill. A single simulation consumes approximately 8,400 kWh and costs \u20ac1,000\u2013\u20ac20,000 to run on HPC infrastructure, taking <a href=\"https:\/\/www.bloomberg.com\/news\/articles\/2026-03-26\/energy-traders-turn-to-ai-to-forecast-the-weather-forecast?embedded-checkout=true\" target=\"_blank\">one to two hours per cycle<\/a>. That compute ceiling caps update frequency at two to four runs per day, a hard constraint the energy industry has accepted for forty years.<\/p>\n<p>EPT-2 changes that cost profile. A single inference <a href=\"https:\/\/www.bloomberg.com\/news\/articles\/2026-03-26\/energy-traders-turn-to-ai-to-forecast-the-weather-forecast?embedded-checkout=true\" target=\"_blank\">runs on a single GPU in minutes at approximately 0.25 kWh<\/a> and $0.20\u2013$15 per simulation. EPT-2 was trained on 8 \u00d7 H100 GPUs over 10 days, while Microsoft Aurora required 32 \u00d7 A100 GPUs over 18 days. The inference cost delta is roughly four orders of magnitude.<\/p>\n<p>That efficiency unlocks higher update frequency. EPT2-RR, Jua&#8217;s rapid-refresh variant, updates up to 24 times per day. A typical Jua run completes approximately 2.5 hours ahead of competing operational runs at the same cycle, which creates earlier and more frequent intraday trade windows.<\/p>\n<p>The operational cost advantage also translates into platform capability. Inside Jua for Energy, the Jua platform benchmarks more than 25 models on any region, variable, and time window, returning a head-to-head comparison in seconds. The set includes 10 proprietary AI models from the EPT family and 15 third-party NWP and AI models such as ECMWF HRES, ECMWF ENS, ECMWF AIFS, NOAA GFS, Microsoft Aurora, and GFS GraphCast. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">This benchmarking infrastructure now serves major utilities across five continents, including Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Qu\u00e9bec.<\/a><\/p>\n<h2>Head-to-Head Accuracy Comparison Across Key Variables<\/h2>\n<p>The table below summarizes EPT-2 and EPT-2e performance against ECMWF HRES and ECMWF ENS across the four primary energy-relevant variables and three lead-time bands, as documented in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. All figures are directional results from the peer-reviewed technical report. RMSE and CRPS deltas favor EPT-2 and EPT-2e in every cell.<\/p>\n<table>\n<thead>\n<tr>\n<th>Variable<\/th>\n<th>Lead-Time Band<\/th>\n<th>EPT-2 vs. ECMWF HRES (RMSE)<\/th>\n<th>EPT-2e vs. ECMWF ENS Mean (RMSE &amp; CRPS)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>10 m Wind Speed<\/td>\n<td>0\u201372 h (short range)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 outperforms HRES across full range<\/a><\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats ENS mean on RMSE and CRPS<\/a><\/td>\n<\/tr>\n<tr>\n<td>10 m Wind Speed<\/td>\n<td>72\u2013168 h (medium range)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 outperforms HRES across full range<\/a><\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats ENS mean on RMSE and CRPS<\/a><\/td>\n<\/tr>\n<tr>\n<td>100 m Wind Speed (hub height)<\/td>\n<td>0\u2013240 h (full range)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 outperforms HRES across full range<\/a><\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats ENS mean at virtually every lead time<\/a><\/td>\n<\/tr>\n<tr>\n<td>2 m Temperature<\/td>\n<td>0\u2013240 h (full range)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 outperforms HRES across full range<\/a><\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats ENS mean at virtually every lead time<\/a><\/td>\n<\/tr>\n<tr>\n<td>Surface Solar Radiation (SSRD)<\/td>\n<td>0\u2013240 h (full range)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 outperforms HRES across full range<\/a><\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e beats ENS mean at virtually every lead time<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Microsoft Aurora has no SSRD output, so direct comparison on solar radiation is impossible. On 10 m wind, 100 m wind, and 2 m temperature, EPT-2 beats Aurora across the full 0\u2013240 hour range, as documented in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>. EPT-2 inference is approximately 25% faster than Aurora. The financial implication is concrete. A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves about \u20ac1.5 M per year under typical hedging and penalty structures. A 1 GW solar portfolio at four percentage points of accuracy gain saves approximately \u20ac3 M per year.<\/p>\n<h2>How to Evaluate Any AI Weather Claim<\/h2>\n<p>Three criteria separate verifiable AI weather forecast accuracy from marketing claims. First, ground-station validation at scale sets the standard. Benchmarks run against more than 10,000 real surface observations, with no post-processing or station fine-tuning, capture the conditions that actually drive P&amp;L. Vendor-provided graphics against gridded reanalysis do not.<\/p>\n<p>Second, transparent ensemble scores matter. CRPS, not just RMSE, is the appropriate metric for probabilistic models, because energy risk management requires calibrated uncertainty quantification. Any vendor that cannot produce CRPS tables against ECMWF ENS is not operating at production grade.<\/p>\n<p>Third, live proof-of-value protects against cherry-picking. Static slides can highlight only favorable conditions, so the benchmark should be reproducible by the evaluator on their own region and variable. Jua for Energy&#8217;s benchmarking surface runs more than 25 models on any region and variable and returns a head-to-head result in seconds. Meteorologists at Axpo, Statkraft, and EnBW used this same surface during their own evaluations.<\/p>\n<p>Run benchmarks on your own region and variables on the Jua platform. See your forecasts head-to-head against the full model set at <a href=\"https:\/\/athena.jua.ai\" target=\"_blank\">athena.jua.ai<\/a>.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the most accurate weather forecasting AI?<\/h3>\n<p>As of the EPT-2 technical report published in 2025, EPT-2 is the global state of the art in atmospheric prediction. It outperforms ECMWF HRES, Microsoft Aurora, Google DeepMind GraphCast, NOAA GFS, and ECMWF AIFS on the four energy-critical variables across the full forecast range documented earlier. The evaluation uses more than 10,000 real ground stations on open-source StationBench with no post-processing. EPT-2e, the ensemble variant, also exceeds ECMWF ENS performance on probabilistic skill. EPT-2 is the foundation model at the core of Jua for Energy, Jua&#8217;s first applied product.<\/p>\n<h3>What are the disadvantages of AI in weather forecasting?<\/h3>\n<p>AI weather models can suffer from physics inconsistency, localized extreme-event underperformance, and long-range error accumulation. Standard transformers applied to atmospheric data can violate conservation laws, and models trained on coarse grids may miss sub-grid phenomena such as orographic wind acceleration. Autoregressive rollouts often compound error at longer lead times. Physics-constrained foundation models like EPT-2 address these architectural issues, as described in the core risks section above.<\/p>\n<h3>How does EPT-2 compare with ECMWF on extreme events?<\/h3>\n<p>EPT-2 is benchmarked against more than 10,000 real ground stations with no post-processing, so the evaluation captures actual observed conditions rather than smoothed reanalysis fields. The physics constraints built into EPT-2&#8217;s architecture, including conservation of mass, momentum, and energy learned from observational data, provide a structural floor that prevents physically implausible outputs during anomalous conditions. EPT-2 outperforms ECMWF HRES across all four primary energy variables and lead times, and EPT-2e delivers better-calibrated uncertainty for tail-risk events.<\/p>\n<h3>Can AI models run fast enough for intraday energy trading?<\/h3>\n<p>AI weather models now run fast enough for intraday trading at a cost that traditional NWP cannot match. As detailed in the operational cost section, EPT-2 runs on a single GPU in minutes at a fraction of the energy and time required for traditional NWP simulations. EPT2-RR, Jua&#8217;s rapid-refresh variant, updates up to 24 times per day. Actual-generation power forecasts inside Jua for Energy refresh every 15 minutes.<\/p>\n<p>Divergence alerts fire the moment two models disagree on a key variable, and correction alerts fire the moment a model revises its own output. These features surface intraday trade windows without requiring the trader to monitor the platform continuously. The constraint on intraday forecast frequency has been a compute ceiling, not a modeling ceiling, and AI inference removes that ceiling.<\/p>\n<h2>Conclusion: Put EPT-2 to Work on Your Own Region<\/h2>\n<p>The EPT-2 technical report establishes a clear benchmark. A physics-constrained foundation model now outperforms ECMWF HRES on every variable and lead time relevant to energy trading, delivers ensemble skill that exceeds the 50-member ECMWF ENS mean, and runs at approximately 0.25 kWh per inference on a single GPU. Jua is a foundation model and agent company, and Jua for Energy is the first applied product built on EPT and Athena.<\/p>\n<p>The Jua platform benchmarks more than 25 models in seconds on any region and variable, the same live comparison that closes evaluations at Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Qu\u00e9bec. The numbers speak. Run them on your own region.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>See EPT-2 head-to-head against your current provider<\/strong><\/a> and quantify the impact on your trading P&amp;L.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Jua&#8217;s EPT-2 outperforms ECMWF HRES on every variable &amp; lead time. Get 24 daily updates on wind, temperature &amp; solar. Benchmark EPT-2 on your data now.<\/p>\n","protected":false},"author":103,"featured_media":306,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"class_list":["post-307","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-weather-forecasting"],"_links":{"self":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/307","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/comments?post=307"}],"version-history":[{"count":2,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/307\/revisions"}],"predecessor-version":[{"id":734,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/307\/revisions\/734"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media\/306"}],"wp:attachment":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media?parent=307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/categories?post=307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/tags?post=307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}