{"id":550,"date":"2026-06-09T05:17:27","date_gmt":"2026-06-09T05:17:27","guid":{"rendered":"https:\/\/jua.ai\/articles\/ai-weather-vs-gfs\/"},"modified":"2026-06-09T05:17:27","modified_gmt":"2026-06-09T05:17:27","slug":"ai-weather-vs-gfs","status":"publish","type":"post","link":"https:\/\/jua.ai\/articles\/ai-weather-vs-gfs\/","title":{"rendered":"AI Weather vs GFS: EPT-2 Leads on Every Energy Variable"},"content":{"rendered":"<p><em>Written by: Olivier Lam, Physical AI Team, Jua.ai AG<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways for Energy Desks<\/h2>\n<ul>\n<li>EPT-2 outperforms GFS and ECMWF HRES on every energy-relevant variable at all lead times from 0 to 240 hours, based on StationBench benchmarks against more than 10,000 real ground stations.<\/li>\n<li>Physics-constrained foundation models like EPT-2 deliver forecasts at roughly 0.25 kWh and $0.20\u2013$15 per inference, which is about four orders of magnitude cheaper than a single GFS simulation.<\/li>\n<li>EPT-2 RR supports up to 24 daily updates versus GFS\u2019s four cycles, so intraday traders are no longer locked into six-hour windows of stale forecasts.<\/li>\n<li>By embedding conservation laws for mass, momentum, and energy, EPT-2 maintains accuracy on extreme events where purely data-driven AI models typically underestimate intensity, which supports tail-risk trading decisions.<\/li>\n<li><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Book a demo<\/strong><\/a> with Jua to run live EPT-2 benchmarks against your current forecast provider in under five minutes and see the accuracy, cost, and frequency advantages firsthand.<\/li>\n<\/ul>\n<h2>How EPT-2 Differs from GFS in Practice<\/h2>\n<p>GFS (Global Forecast System) is a physics-based NWP model operated by NOAA. It decomposes the atmosphere into grid cells and solves differential equations inside each one, a method that has operated reliably for decades but carries a fixed computational ceiling. EPT-2 is a physics-constrained foundation model that learns governing dynamics directly from observational data in a latent representation integrated forward in time. It produces forecasts at native any-\u0394t, which means arbitrary lead times without iterative rolling, and this structure removes the error compounding that appears when models like Aurora roll forward in fixed 6-hour steps.<\/p>\n<p>RMSE (root mean square error) measures the average magnitude of forecast error against observed values, and lower values indicate better performance. CRPS (continuous ranked probability score) measures the skill of probabilistic ensemble forecasts, and again lower values are better. Lead time describes how far ahead a forecast is valid. Hindcast refers to retrospective forecasts run against historical observations for benchmarking.<\/p>\n<table>\n<thead>\n<tr>\n<th>Method<\/th>\n<th>Accuracy (RMSE\/CRPS, energy variables)<\/th>\n<th>Update Frequency<\/th>\n<th>Inference Cost<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>GFS (NOAA NWP)<\/td>\n<td>Baseline; <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">outperformed by EPT-2 on 10 m wind, 100 m wind, 2 m temperature, and SSRD at all lead times 0\u2013240 h<\/a><\/td>\n<td>4 cycles per day<\/td>\n<td><a href=\"https:\/\/jua.ai\/articles\/weather-data-api-energy-trading\" target=\"_blank\">~8,400 kWh per simulation; \u20ac1,000\u2013\u20ac20,000 on HPC<\/a><\/td>\n<\/tr>\n<tr>\n<td>EPT-2 (Jua, deterministic)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">State of the art on all four energy-relevant variables across 0\u2013240 h; outperforms ECMWF HRES and Microsoft Aurora<\/a><\/td>\n<td>Up to 4 cycles per day (deterministic flagship); EPT-2 RR up to 24\u00d7\/day<\/td>\n<td><a href=\"https:\/\/jua.ai\/articles\/weather-data-api-energy-trading\" target=\"_blank\">~0.25 kWh; $0.20\u2013$15 per simulation on a single GPU<\/a><\/td>\n<\/tr>\n<tr>\n<td>EPT-2e (Jua, ensemble)<\/td>\n<td><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">Surpasses ECMWF ENS mean on RMSE and CRPS at virtually every lead time<\/a><\/td>\n<td>4 cycles per day<\/td>\n<td>Fraction of NWP ensemble cost, single-GPU inference<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Benchmarks use <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">StationBench methodology<\/a>, which evaluates models against more than 10,000 real ground-truth weather stations with no post-processing or station fine-tuning, so results remain directly comparable across models.<\/p>\n<h2>EPT-2 Accuracy on Energy-Relevant Variables<\/h2>\n<p><a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 sets a new state of the art on every energy-relevant variable evaluated in arXiv:2507.09703<\/a>. The variables are 10 m wind speed, 100 m wind speed, 2 m temperature, and surface solar radiation (SSRD). The improvement holds at every lead time from 0 to 240 hours, which covers intraday, day-ahead, and multi-day horizons that map directly to the trade windows energy desks use.<\/p>\n<p>The StationBench evaluation methodology anchors these results to physical reality. Scores are computed against observations from more than 10,000 surface stations globally, with no model-specific post-processing applied. This approach removes the common failure mode in vendor-provided accuracy graphics, where results are tuned to the evaluation set.<\/p>\n<p>For wind, the 100 m variable is operationally critical. It corresponds to the hub height of modern wind turbines, and errors at this level propagate directly into generation forecasts and imbalance costs. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 outperforms Microsoft Aurora on both 10 m and 100 m wind across the full 0\u2013240 h range<\/a>. Aurora produces no SSRD output, so EPT-2 fills that gap by default for solar-generation forecasting.<\/p>\n<p>EPT-2e, the ensemble variant of EPT-2, extends this accuracy advantage into probabilistic forecasting. An ensemble, a set of perturbed model runs that samples forecast uncertainty, is the standard tool for risk quantification in energy trading. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2e surpasses the ECMWF ENS mean on both RMSE and CRPS at virtually every lead time<\/a>, with 10 published members against the ENS&#8217;s 50, at a fraction of the computational cost.<\/p>\n<h2>Update Frequency and Trading Economics<\/h2>\n<p>GFS runs four cycles per day, each consuming approximately <a href=\"https:\/\/jua.ai\/articles\/weather-data-api-energy-trading\" target=\"_blank\">8,400 kWh of compute at a cost of \u20ac1,000\u2013\u20ac20,000<\/a>. The economics of high-performance computing infrastructure make higher cadence structurally impossible at that cost level. Between runs, traders operate on stale numbers, a constraint the energy industry has accepted for forty years because no alternative existed.<\/p>\n<p>EPT-2 RR, Jua&#8217;s rapid-refresh model variant, supports up to 24 daily updates. A single EPT-2 inference runs on a single GPU in minutes at approximately <a href=\"https:\/\/jua.ai\/articles\/weather-data-api-energy-trading\" target=\"_blank\">0.25 kWh and $0.20\u2013$15<\/a>. This cost gap reaches roughly four orders of magnitude. NOAA\u2019s own AIGFS model produces forecasts using a fraction of the computing resources required by the operational GFS, which confirms the structural cost asymmetry between AI inference and NWP at the institutional level.<\/p>\n<p>For intraday energy trading, update frequency directly affects P&amp;L. A wind ramp that appears in a model update at 10:00 a.m. but is not visible until the next GFS cycle at 12:00 p.m. creates a two-hour window in which the market re-prices before the trader can act. Hourly AI updates reduce trader exposure to forecast surprises, imbalance costs, and extreme price events by capturing weather-driven changes hours earlier than traditional numerical models.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Book a demo<\/strong> and run live benchmarks on your own region and variables in under five minutes.<\/a><\/p>\n<h2>Extreme Events and Model Trust<\/h2>\n<p>Energy desks care most about model behavior during extremes, and purely data-driven AI models have struggled here. A 2026 study led by Karlsruhe Institute of Technology found that purely data-driven AI models including GraphCast, Pangu-Weather, and Fuxi consistently underestimate the intensity of record-breaking heat, cold, and wind events, with errors growing as the event exceeds the training distribution. The study recommends hybrid approaches and identifies physics-based models as indispensable for the most extreme events.<\/p>\n<p>EPT-2 is not a purely data-driven model. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">EPT-2 incorporates physical structure and constraints while learning from data<\/a>. Conservation laws governing mass, momentum, and energy are embedded in the architecture, not learned as soft patterns. This structure defines the distinction between a physics-constrained foundation model and a generic neural network applied to weather data.<\/p>\n<p>The validation methodology reinforces this position through the StationBench approach described earlier. Results are published in peer-reviewed technical reports on arXiv (<a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">2507.09703<\/a>), not only in vendor-controlled graphics. Meteorologists who were sceptical of AI accuracy claims often become internal champions once they run the benchmark themselves on the Jua platform.<\/p>\n<h2>How to Blend EPT-2 with GFS<\/h2>\n<p>Jua for Energy does not remove GFS or ECMWF from the stack, it changes how they are used. Serious customers keep their existing NWP subscriptions and run EPT-2 alongside them. The operational case for maintaining GFS in the stack is ensemble diversity, because two models with different architectures and different error structures provide a richer signal than either alone, especially for tail-risk events where no single model is authoritative.<\/p>\n<p>EPT-2 replaces the workflow infrastructure built around raw NWP outputs. That infrastructure includes the in-house grib-file pipeline, the manual benchmarking harness, the morning-briefing analyst, and the dashboard stitching across a dozen vendor screens. The Jua platform exposes 25+ models, including ECMWF HRES, ECMWF ENS, NOAA GFS, Microsoft Aurora, and GFS GraphCast, through a single schema and a single API. Swapping or comparing models no longer requires re-engineering pipelines.<\/p>\n<p>Energy traders can follow a simple pattern. Retain GFS and ECMWF for ensemble diversity and regulatory defensibility. Replace the plumbing with the Jua platform. Use EPT-2 as the primary deterministic signal and EPT-2e for probabilistic positioning. The divergence between EPT-2 and GFS becomes a tradeable signal, because when the two models disagree on a key variable, the Jua platform fires a divergence alert before the broader market reprices.<\/p>\n<h2>Energy-Trading Impacts of EPT-2<\/h2>\n<p>Three operational advantages flow directly from the accuracy and frequency data above.<\/p>\n<p><strong>Divergence alerts as trade signals.<\/strong> When EPT-2 and GFS disagree on 100 m wind or 2 m temperature in a key zone, the disagreement acts as a forward indicator of price movement because the market typically reprices once the consensus forecast shifts. To capture this edge, the Jua platform fires divergence alerts the moment two models separate on a key variable, filterable by zone and production source resource (PSR) type, so the trader sees the signal before the broader market reprices.<\/p>\n<p><strong>Rapid-refresh surfaces for intraday positioning.<\/strong> EPT-2 RR updates up to 24 times per day. Actual-generation power forecasts on the Jua platform refresh every 15 minutes. For intraday gas and power markets, where the trade window is measured in minutes rather than hours, the cadence difference between 4 GFS cycles and 24 EPT-2 RR cycles often separates acting from reacting.<\/p>\n<p><strong>Power-forecast accuracy at portfolio scale.<\/strong> A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately \u20ac1.5 M per year in hedging and imbalance costs. Solar portfolios see even larger savings, around \u20ac3 M per year at the same accuracy gain, because solar generation is more sensitive to forecast error during peak pricing hours. This dynamic makes EPT-2&#8217;s SSRD outperformance particularly valuable for solar-heavy portfolios, especially since Aurora produces no SSRD output at all. These savings explain why customers including Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Qu\u00e9bec now execute daily trading decisions on the Jua platform across four continents.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How does EPT-2 perform at 48\u2013120 hour lead times?<\/h3>\n<p>EPT-2 outperforms GFS on 10 m wind speed, 100 m wind speed, and 2 m temperature at every lead time from 0 to 240 hours, including the 48\u2013120 hour window that covers day-ahead and multi-day trading horizons. The evaluation uses StationBench methodology, with more than 10,000 real ground-truth weather stations globally and no post-processing or station fine-tuning applied to either model. The 48\u2013120 hour range is operationally significant because it covers the day-ahead auction window and the multi-day horizon used for generation scheduling and gas storage decisions. EPT-2&#8217;s native any-\u0394t architecture avoids the rolling error that accumulates in models that step forward in fixed 6-hour increments.<\/p>\n<h3>What is the inference cost difference between AI weather models and GFS?<\/h3>\n<p>A single GFS simulation consumes approximately 8,400 kWh of compute and costs \u20ac1,000\u2013\u20ac20,000 to run on HPC infrastructure, taking one to two hours to complete. A single EPT-2 inference runs on a single GPU in minutes at approximately 0.25 kWh and $0.20\u2013$15. The cost delta reaches roughly four orders of magnitude. NOAA\u2019s own AIGFS model, built on GraphCast and fine-tuned on GFS initial conditions, produces forecasts using a fraction of the computing resources required by the operational GFS, which confirms that the cost asymmetry is structural to the AI-versus-NWP comparison, not specific to any single vendor. For energy traders, the practical consequence is update frequency. At GFS cost levels, four daily cycles sit near the ceiling, while at EPT-2 cost levels, 24 daily updates are operationally feasible.<\/p>\n<h3>Can AI models be trusted for extreme events?<\/h3>\n<p>Purely data-driven AI models, which learn statistical patterns without embedded physical constraints, have documented weaknesses on record-breaking extremes. A 2026 study from Karlsruhe Institute of Technology found that GraphCast, Pangu-Weather, and Fuxi consistently underestimate the intensity of heat, cold, and wind records, with errors growing as events exceed the training distribution. EPT-2 is architecturally different. It is a physics-constrained foundation model that embeds conservation laws for mass, momentum, and energy at the representation level, not as soft learned patterns. This structure means EPT-2 cannot produce outputs that violate the governing equations of the atmosphere in the way a generic neural network can. The validation is external and peer-reviewed, and StationBench results are published in arXiv:2507.09703. For the most extreme tail events, maintaining GFS or ECMWF alongside EPT-2 for ensemble diversity remains the defensible operational posture.<\/p>\n<h3>How many updates per day are now operationally feasible?<\/h3>\n<p>EPT-2 RR supports up to 24 daily updates, one per hour, compared with GFS&#8217;s four cycles per day. This capability is an operational product available through the Jua platform today. The economics make it feasible, because at approximately 0.25 kWh and $0.20\u2013$15 per inference on a single GPU, running 24 cycles per day costs a fraction of a single GFS simulation. Actual-generation power forecasts on the Jua platform refresh every 15 minutes, which extends the cadence advantage into the near-term horizon where intraday markets clear. For energy traders, the operational implication is that the gap between model updates, during which traders are exposed to stale numbers, compresses from six hours to one hour or less.<\/p>\n<h2>Conclusion: Why Energy Desks Adopt EPT-2<\/h2>\n<p>The quantified case for EPT-2 over GFS rests on three independent advantages, each documented in peer-reviewed benchmarks. Accuracy remains the first pillar, with variable-by-variable outperformance across the full forecast horizon documented above. Cost forms the second pillar, with EPT-2 inference running at roughly four orders of magnitude lower compute expense than a GFS simulation. Frequency completes the picture, because EPT-2 RR supports hourly updates instead of the quarterly cycles that have defined exposure to stale forecasts for forty years.<\/p>\n<p>Jua is a foundation model and agent company. EPT-2 is the flagship model in the EPT family, a general physics foundation model fine-tuned for atmospheric prediction. Jua for Energy is the first applied product, combining EPT-2 with Athena, Jua&#8217;s AI agent, in a single platform used by Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Qu\u00e9bec across four continents. The architecture learns physics, and the domain is a variable.<\/p>\n<p><a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\"><strong>Book a demo<\/strong> and run live benchmarks on the Jua platform against your current forecast provider in under five minutes.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Jua&#8217;s EPT-2 outperforms GFS &amp; ECMWF on every energy variable at all lead times. See StationBench data and cut forecast costs by 10,000x.<\/p>\n","protected":false},"author":103,"featured_media":549,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"class_list":["post-550","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\/550","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"}],"author":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/users\/103"}],"replies":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/comments?post=550"}],"version-history":[{"count":0,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/550\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media\/549"}],"wp:attachment":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media?parent=550"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/categories?post=550"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/tags?post=550"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}