{"id":311,"date":"2026-05-08T23:18:47","date_gmt":"2026-05-08T23:18:47","guid":{"rendered":"https:\/\/jua.ai\/articles\/best-weather-prediction-models-2026\/"},"modified":"2026-07-04T05:04:38","modified_gmt":"2026-07-04T05:04:38","slug":"best-weather-prediction-models-2026","status":"publish","type":"post","link":"https:\/\/jua.ai\/articles\/best-weather-prediction-models-2026\/","title":{"rendered":"2026 Weather Prediction Model Leaderboard: EPT-2 Tops ECMWF"},"content":{"rendered":"<p><em>Written by: Olivier Lam, Physical AI Team, Jua.ai AG | Last updated: June 29, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Why EPT-2 Matters for Energy Traders in 2026<\/h2>\n<ul>\n<li>EPT-2 now leads ECMWF HRES across 0\u2013240 hours on 10 m wind, 100 m wind, 2 m temperature, and surface solar radiation, the core energy P&amp;L drivers.<\/li>\n<li>EPT-2e, Jua\u2019s ensemble variant, delivers stronger probabilistic skill than the 50-member ECMWF ENS mean at most lead times, widening the gap between AI and traditional NWP.<\/li>\n<li>AI peers such as Microsoft Aurora and Google DeepMind GraphCast trail EPT-2 on the variables they cover and currently lack productised ensembles or refresh schedules that match EPT-2 RR\u2019s 24 runs per day.<\/li>\n<li>EPT-2 RR delivers up to 24 updates daily. Its ~0.25 kWh per simulation cost makes those frequent runs practical while still maintaining higher resolution and earlier delivery than traditional NWP.<\/li>\n<li>Energy traders can benchmark these results on the Jua platform in minutes; <a href=\"https:\/\/meetings-eu1.hubspot.com\/guett\/energy-trading?uuid=d780665f-ff71-439c-addf-c80e49af0627\" target=\"_blank\">book a personalized demo with Jua<\/a> to see the accuracy edge on your own region and variables.<\/li>\n<\/ul>\n<h2>ECMWF vs GFS vs EPT-2 for Trading Decisions<\/h2>\n<p>For specific energy-sector variables and lead times, such as day-ahead wind speed at particular sites, ECMWF HRES can outperform NOAA GFS on deterministic skill. <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\">ECMWF\u2019s two-week outlook remains the definitive reference point for traders repricing risk around heating demand, renewable output, and system tightness<\/a>. GFS still serves as the free deterministic baseline and adds redundancy and ensemble diversity.<\/p>\n<p>The more consequential 2026 comparison for energy portfolios is ECMWF versus EPT-2. According to <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a>, EPT-2 now leads ECMWF HRES across the full 0\u2013240 hour range on the four primary energy variables mentioned earlier. ECMWF HRES held the gold standard for forty years. EPT-2 now sets the pace on the variables that matter most to energy traders. Performance of GFS varies by variable and region.<\/p>\n<p>To understand where EPT-2 sits relative to both AI and traditional models, the following leaderboard ranks all major competitors on the metrics that matter for energy trading.<\/p>\n<h2>2026 AI Weather Model Leaderboard for Energy Variables<\/h2>\n<p>The ranking below reflects deterministic skill on energy-relevant variables (10 m wind, 100 m wind, 2 m temperature, surface solar radiation) across 0\u2013240 hour lead times. All models are evaluated against more than 10,000 real ground stations on open-source StationBench with no post-processing or station fine-tuning, as documented in <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a> and <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2410.15076<\/a>.<\/p>\n<ol>\n<li><strong>EPT-2 (Jua)<\/strong> \u2013 Leads ECMWF HRES on every lead time and on all four primary energy variables. Beats Microsoft Aurora on 10 m wind, 100 m wind, and 2 m temperature across the full 0\u2013240 hour range. Aurora produces no surface solar radiation output, so EPT-2 wins by default on SSRD. EPT2-HRRR reaches ~5 km resolution over Europe. EPT2-RR runs up to 24 times per day. Inference runs at ~0.25 kWh per simulation on a single GPU.<\/li>\n<li><strong>ECMWF HRES<\/strong> \u2013 The forty-year benchmark. It runs at 9 km resolution, with 2\u20134 runs per day, and consumes ~8,400 kWh per simulation. It remains the universal reference for regulated utilities and physical trading houses, although it now ranks second to EPT-2 on energy-variable RMSE.<\/li>\n<li><strong>Microsoft Aurora<\/strong> \u2013 Previous state of the art in AI weather before EPT-2. It trails EPT-2 on 10 m wind, 100 m wind, and 2 m temperature across the full 0\u2013240 hour range and offers no SSRD output. A fixed 6-hour roll-forward schedule compounds error at longer lead times. No productised ensemble is available.<\/li>\n<li><strong>GFS GraphCast (Google DeepMind)<\/strong> \u2013 GNN-based model initialised with NOAA GFS data. <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">EPT-1.5 outperforms GraphCast on European wind and temperature<\/a>, and EPT-2 extends that lead. No productised ensemble or operational refresh schedule exists today.<\/li>\n<li><strong>NOAA GFS<\/strong> \u2013 Free deterministic baseline that remains useful for ensemble diversity and redundancy.<\/li>\n<\/ol>\n<h2>How EPT-2e Changes Ensemble Forecasting<\/h2>\n<p>Probabilistic forecasting, including ensemble spread, CRPS, and reliability, shows the widest gap between AI and NWP in 2026. ECMWF ENS, with 50 members, has served as the gold standard for probabilistic NWP for decades. EPT-2e, Jua\u2019s ensemble variant, now 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>. No AI peer, including Aurora, GraphCast, or ECMWF AIFS, currently ships a comparable productised ensemble. For energy traders who need probabilistic wind ramp or solar dip distributions to size positions, EPT-2e represents the current state of the art.<\/p>\n<h2>Model Comparison Matrix by Forecast Horizon<\/h2>\n<p>The table below compares the five models most relevant to energy trading on four operational dimensions. Benchmark figures come from <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a> and <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2410.15076<\/a>. Infrastructure cost figures reflect published operational specifications.<\/p>\n<table>\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Deterministic Accuracy (0\u2013240 h, energy variables)<\/th>\n<th>Ensemble Skill<\/th>\n<th>Update Cadence \/ Inference Cost<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>EPT-2 \/ EPT-2e (Jua)<\/strong><\/td>\n<td>Leads ECMWF HRES on every lead time and beats Aurora on 10 m wind, 100 m wind, and 2 m temperature across the full range. Wins on SSRD by default because Aurora has no SSRD output. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a><\/td>\n<td>EPT-2e surpasses the 50-member ECMWF ENS mean on RMSE and CRPS at virtually every lead time. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a><\/td>\n<td>Up to 24\u00d7\/day (EPT-2 RR) with an EPT-2e ensemble. Around 0.25 kWh and roughly $0.20\u2013$15 per simulation on a single GPU. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Nebius<\/a><\/td>\n<\/tr>\n<tr>\n<td><strong>ECMWF HRES<\/strong><\/td>\n<td>Forty-year benchmark that now ranks second to EPT-2 on energy-variable RMSE across 0\u2013240 h<\/td>\n<td>N\/A (deterministic only; ENS is separate)<\/td>\n<td>2\u20134\u00d7\/day with ~8,400 kWh and \u20ac1,000\u2013\u20ac20,000 per simulation on HPC<\/td>\n<\/tr>\n<tr>\n<td><strong>ECMWF ENS<\/strong><\/td>\n<td>N\/A (probabilistic; deterministic skill via ENS mean)<\/td>\n<td>50-member gold standard, now surpassed by EPT-2e on RMSE and CRPS. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a><\/td>\n<td>2\u20134\u00d7\/day with HPC cost comparable to HRES<\/td>\n<\/tr>\n<tr>\n<td><strong>Microsoft Aurora<\/strong><\/td>\n<td>Trails EPT-2 on 10 m wind, 100 m wind, and 2 m temperature across the full range and offers no SSRD output. <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a><\/td>\n<td>No productised ensemble<\/td>\n<td>Typically 4\u00d7\/day research cadence with inference cost in a similar order of magnitude to EPT-2. EPT-2 runs about 25% faster.<\/td>\n<\/tr>\n<tr>\n<td><strong>GFS GraphCast (DeepMind)<\/strong><\/td>\n<td>Loses to EPT-1.5 on European wind and temperature. <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2410.15076<\/a>. EPT-2 extends that lead.<\/td>\n<td>No productised ensemble<\/td>\n<td>No productised operational refresh schedule; research output only<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Update Frequency and Cost for Real-World Operations<\/h2>\n<p>The energy industry has operated on 2\u20134 global NWP forecasts per day for forty years. That pattern reflects HPC economics: <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">a single traditional NWP simulation consumes approximately 8,400 kWh and costs \u20ac1,000\u2013\u20ac20,000 to run<\/a>. The European supercomputer can run its full algorithm twice a day. With supplementary runs, the industry receives roughly four global forecasts per 24 hours. Between runs, traders often rely on stale numbers.<\/p>\n<p>EPT-2 RR, Jua\u2019s rapid-refresh variant, updates up to 24 times per day. EPT-2e provides the ensemble view. A single EPT-2 inference runs at approximately 0.25 kWh and $0.20\u2013$15 on a single GPU, in minutes, which is roughly four orders of magnitude cheaper than an equivalent NWP 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 cost asymmetry at training time mirrors the asymmetry at inference time. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Jua delivers hourly global weather updates at 6\u00d7 higher resolution than comparable AI models<\/a>, and a typical Jua run completes about 2.5 hours ahead of competing operational runs at the same cycle.<\/p>\n<p><a href=\"https:\/\/athena.jua.ai\" target=\"_blank\">Run benchmarks on your own region and variables on the Jua platform. See your forecasts side by side against 25+ models at athena.jua.ai<\/a>.<\/p>\n<h2>Running Your Own Benchmark on Athena<\/h2>\n<p>Jua is a foundation model and agent company, and Jua for Energy is the first applied product. Its live benchmarking surface gives the fastest path from scepticism to a procurement decision. The platform at <a href=\"https:\/\/athena.jua.ai\" target=\"_blank\">athena.jua.ai<\/a> puts more than 25 models on a single surface: 10 proprietary AI models from the EPT family plus 15 third-party NWP and AI models, including ECMWF HRES, ECMWF ENS, ECMWF AIFS, NOAA GFS, GFS GraphCast, Microsoft Aurora, DWD ICON Global, and ICON-EU.<\/p>\n<p>A meteorologist or quant trader selects a region, a variable, and a time window. The platform then returns a head-to-head accuracy comparison in a few minutes. EPT2-HRRR reaches ~5 km resolution over Europe, and the Jua for Energy product delivers up to 1 km resolution. Athena, Jua\u2019s AI agent currently instrumented with the Jua for Energy tool surface, resolves follow-up queries such as briefings, backtests, and custom widgets in approximately 90 seconds. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Jua serves major utilities across four continents, including some of Europe\u2019s largest energy companies, as well as commodity traders and hedge funds<\/a>, with customers including Axpo, TotalEnergies, Statkraft, EnBW, EDF, and Hydro-Qu\u00e9bec. The benchmark usually acts as the deal trigger because the numbers speak clearly.<\/p>\n<h2>Key Takeaways for Energy Traders by Variable<\/h2>\n<p><strong>Wind (10 m and 100 m):<\/strong> EPT-2 leads ECMWF HRES on RMSE 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>. For hub-height wind forecasting, the variable most directly tied to wind generation P&amp;L, EPT-2 currently holds the accuracy lead. EPT-2 RR refreshes up to 24 times per day, surfacing wind ramp events hours before the next NWP run lands. <a href=\"https:\/\/nebius.com\/customer-stories\/jua\" target=\"_blank\">Jua\u2019s forecasts carry an estimated $1.5 million P&amp;L impact per gigawatt annually in European energy markets<\/a>.<\/p>\n<p><strong>Temperature (2 m):<\/strong> EPT-2 beats ECMWF HRES and Microsoft Aurora on 2 m temperature across the full 0\u2013240 hour range. For gas demand and load forecasting, where temperature is the primary driver, EPT-2 is the deterministic leader. EPT-2e provides probabilistic temperature distributions with the ensemble superiority documented earlier, which helps traders size positions around cold-snap or heat-wave tail risk.<\/p>\n<p><strong>Solar radiation (SSRD):<\/strong> EPT-2 outperforms ECMWF HRES on surface solar radiation across 0\u2013240 hours. Microsoft Aurora produces no SSRD output. For solar generation forecasting, where SSRD is the direct physical input, EPT-2 is the only AI model in the 2026 leaderboard with a verified SSRD benchmark. A 1 GW solar portfolio that gains four percentage points of forecast accuracy saves approximately \u20ac3 million per year under typical hedging and penalty structures.<\/p>\n<p><strong>Ensemble and probabilistic horizons:<\/strong> EPT-2e maintains the ensemble advantage over the 50-member ECMWF ENS mean on RMSE and CRPS at virtually every lead time. No AI peer currently ships a comparable productised ensemble. For traders who need spread distributions to size positions or manage imbalance risk, EPT-2e is the current state of the art.<\/p>\n<h2>What the 2026 Leaderboard Means for Your Stack<\/h2>\n<p>The 2026 weather prediction model leaderboard has a clear structure for energy use cases. EPT-2 leads ECMWF HRES on deterministic skill across 0\u2013240 hours on every variable that drives energy P&amp;L. EPT-2e leads the 50-member ECMWF ENS mean on ensemble skill. Microsoft Aurora and GFS GraphCast trail EPT-2 on the variables they cover, and neither ships a productised ensemble or an operational refresh schedule that matches EPT-2 RR\u2019s 24-runs-per-day cadence. ECMWF HRES and ENS remain essential reference signals. Jua for Energy runs alongside them, not instead of them, and displaces the plumbing around the incumbent feed rather than the feed itself.<\/p>\n<p>Jua is a foundation model and agent company. Jua for Energy is the first applied product, built on EPT, a general physics foundation model, and Athena, an AI agent currently instrumented with the energy-trader tool surface. The architecture learns physics, and the domain becomes a variable. The benchmark numbers are published in peer-reviewed technical reports at <a href=\"https:\/\/arxiv.org\/abs\/2507.09703\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2507.09703<\/a> and <a href=\"https:\/\/arxiv.org\/abs\/2410.15076\" target=\"_blank\" rel=\"noindex nofollow\">arXiv:2410.15076<\/a>, and traders can verify them on the live platform in a few minutes.<\/p>\n<p><a href=\"https:\/\/athena.jua.ai\" target=\"_blank\">Run benchmarks on your own region and variables on the Jua platform and compare your forecasts head to head against 25+ models at athena.jua.ai.<\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>What is the most accurate weather prediction model for energy trading in 2026?<\/h3>\n<p>EPT-2, the deterministic flagship of Jua\u2019s Earth Physics Transformer family, is the most accurate weather prediction model for energy-relevant variables in 2026. As documented earlier, it leads ECMWF HRES across all four primary energy variables at every forecast horizon and also beats Microsoft Aurora on the variables Aurora covers. EPT-2e, the ensemble variant, maintains the documented advantage over the 50-member ECMWF ENS mean on RMSE and CRPS at virtually every lead time. Both results appear in peer-reviewed technical reports on arXiv (2507.09703 and 2410.15076), evaluated against more than 10,000 real ground stations with no post-processing or station fine-tuning.<\/p>\n<h3>Is ECMWF still worth using if EPT-2 outperforms it?<\/h3>\n<p>ECMWF HRES and ENS remain essential reference signals for regulated utilities, physical trading houses, and quantitative funds. Jua for Energy does not replace ECMWF, it runs alongside it. ECMWF AIFS, ECMWF\u2019s own AI model, even runs natively on the Jua for Energy platform. Jua for Energy instead displaces the plumbing around the ECMWF feed, such as the in-house grib pipeline, manual benchmarking, morning-briefing routine, and spreadsheet stitching. Customers who run Jua for Energy alongside their existing ECMWF subscription gain EPT-2\u2019s accuracy advantage, EPT-2 RR\u2019s 24-runs-per-day cadence, and a single workspace where every model, including ECMWF, GFS, Aurora, and EPT, appears on the same screen with one schema and one API. The 7\u20139 a.m. manual prep routine compresses into a single workspace open before the market does.<\/p>\n<h3>How does EPT-2 differ from Microsoft Aurora and Google DeepMind GraphCast?<\/h3>\n<p>The first difference is categorical. Aurora and GraphCast are research outputs from large companies\u2019 AI labs and do not ship as foundation models with agents on top of them. Jua is a foundation model and agent company, and Jua for Energy is a productised platform where Aurora and GraphCast run as guests on the comparison surface. Five concrete product-level differences follow. First, EPT-2 forecasts at arbitrary lead times natively, while Aurora rolls forward in fixed 6-hour steps, which compounds error at longer horizons. Second, EPT-2e is a productised ensemble with the documented advantage over the 50-member ECMWF ENS mean, and no AI peer ships an equivalent. Third, EPT-2 RR refreshes up to 24 times per day, while AI peers typically update four times per day on a research cadence. Fourth, Athena, Jua\u2019s AI agent, turns natural-language questions into briefings, benchmarks, backtests, and custom widgets in approximately 90 seconds, and no AI weather peer currently offers anything equivalent. Fifth, Aurora produces no surface solar radiation output, which makes EPT-2 the only AI model in the 2026 leaderboard with a verified SSRD benchmark across 0\u2013240 hours.<\/p>\n<h3>Can I run my own benchmark without a sales call?<\/h3>\n<p>Yes. The live benchmarking surface at athena.jua.ai puts more than 25 models on a single platform, including 10 proprietary AI models from the EPT family and 15 third-party NWP and AI models. A meteorologist or quant trader selects a region, a variable, and a time window, and the platform returns a head-to-head accuracy comparison in a few minutes. No sales call is required to run the benchmark. Quant developers can also install the Python SDK with pip install jua and run backtests against years of historical forecasts programmatically. Athena resolves follow-up queries, including briefings, backtests, and custom widgets, in approximately 90 seconds. The benchmark usually becomes the deal trigger, and the objection shifts from \u201cis this real?\u201d to \u201chow fast can we procure?\u201d once the numbers appear on screen.<\/p>\n<h3>What is the financial impact of switching to a more accurate weather model for an energy portfolio?<\/h3>\n<p>A 1 GW wind portfolio that gains four percentage points of forecast accuracy saves approximately \u20ac1.5 million per year under typical hedging and imbalance penalty structures. A 1 GW solar portfolio at the same accuracy gain saves approximately \u20ac3 million per year, which shows that solar portfolios see roughly double the impact. Customers operating multi-gigawatt portfolios scale these economics linearly. The accuracy gain is not hypothetical, because EPT-2\u2019s lead over ECMWF HRES on every energy-relevant variable has been evaluated against more than 10,000 real ground stations with no post-processing. Traders can verify the benchmark on the live platform at athena.jua.ai.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>See how Jua&#8217;s EPT-2 outperforms ECMWF HRES &amp; GFS on wind, temperature &amp; solar. Explore the best weather prediction models for energy trading.<\/p>\n","protected":false},"author":103,"featured_media":310,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"class_list":["post-311","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\/311","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=311"}],"version-history":[{"count":2,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/311\/revisions"}],"predecessor-version":[{"id":730,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/posts\/311\/revisions\/730"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media\/310"}],"wp:attachment":[{"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/media?parent=311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/categories?post=311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jua.ai\/articles\/wp-json\/wp\/v2\/tags?post=311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}