HiRO-ACEv2: Simulating extreme weather at 3 km resolution for a fraction of the cost
摘要
Ai2 发布 HiRO-ACE 系统的新版本 HiRO-ACEv2,其降尺度模型 HiROv2 可同时预测多种高分辨率变量,在给定粗化的 X-SHiELD 数据时,对极端降雨、风速和气压的高分辨率统计量误差在 3% 以内,并能估算热带气旋总能量。
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同一新闻,入选展示 《Ai2 发布 HiRO-ACEv2,以约三百分之一成本生成全球 3 公里气候数据》
正文 · 原文
We introduce HiRO-ACEv2, the newest update to our High Resolution Output Ai2 Climate Emulator (HiRO-ACE) system. HiROv2 can reproduce even extremely rare rain, wind, and pressure events with high accuracy, while the full system can generate global 3 km climate data at about 1/300th the cost of X-SHiELD, a high-resolution global atmospheric model that explicitly simulates storms and other small-scale weather.
HiRO-ACE, introduced earlier in 2026, has two parts: ACE, an AI model that operates at a coarser 100 km resolution, and HiRO, a model that sharpens those coarse outputs into fine 3 km detail. The biggest change in HiRO-ACEv2 is the new downscaling model, HiROv2, which can now 1) predict several high-resolution fields at once instead of just rainfall, so it can capture a much wider range of extreme weather, and 2) has improved skill in predicting extreme events.
Our main focus with HiRO-ACEv2 was to simulate extreme events well. Changes to how we train HiROv2 improved its ability to reproduce the most intense rain, wind, and pressure values. When given coarsened X-SHiELD data, it matches the target simulation's high-resolution statistics to within 3%, even for events so rare they occur only once in a million data points. It also accurately estimates the total energy of tropical cyclones, including storms it never saw during training, and generates hurricanes with realistic eyes and rain bands, though winds and pressure around the eye remain somewhat more lopsided than in X-SHiELD. Additionally, HiROv2 realistically generates strong winds flowing down mountain slopes.
We also trained an updated version of ACE for this system. Over a 10-year simulation, its hurricanes, average climate patterns, and overall distributions of rain, wind, and pressure all line up well with X-SHiELD. That tells us ACE's coarse simulations are a solid foundation for producing long-term high-resolution climate data.
Putting HiROv2 and ACE together, we generated a full year of global 3 km data. Its statistics closely match X-SHiELD's, even though HiRO was working from a simulation ACE ran on its own rather than from real X-SHiELD data. Across a 10-year run, the energy of HiRO-ACEv2's hurricanes is comparable to X-SHiELD's over the same period, and snapshots from that run show a variety of other extreme events that resemble those in X-SHiELD.
Overall, HiRO-ACEv2 offers researchers a flexible and inexpensive way to generate many versions of high-resolution rain, wind, and pressure data for extreme events over 10 years or more. Producing global high-resolution data with it costs roughly 300 times less than running X-SHiELD. If only one region is needed, the savings can be more than a hundred times greater, depending on the size of the area. We're also working on a streamlined version of HiROv2 that could make it more than ten times faster still.
We still have future work ahead of us: because HiRO learned from X-SHiELD, it only works with the version of ACE tuned to match X-SHiELD. That means it can't yet produce high-resolution data for other climates, such as a warmer future or the historical record in datasets like ERA5. Next, we plan to train on new X-SHiELD datasets, including one simulating a world 4°C warmer, so the system can handle different climates. We also plan to add outputs like near-surface temperature and snow and ice, which would allow for even more uses, such as snowpack prediction.
Visualizing and using HiRO outputs
To make it easy to see what HiRO produces without running any inference, we built a web demo that shows pre-generated precipitation, wind, and sea-level pressure output from HiROv1 and HiROv2 side by side with X-SHiELD, the simulation they were trained on. The demo complements the quantitative comparisons in our tech report: it offers a quick visual check of how the outputs look, and lets users zoom in on their own region of interest to judge performance there.
For more in-depth analysis, the models are available to download, and our tutorial notebooks walk through how to run them.
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来源:Ai2 / Allen Institute for AI · allenai.org
引用原文
Ai2 / Allen Institute for AI. HiRO-ACEv2: Simulating extreme weather at 3 km resolution for a fraction of the cost[EB/OL]. (2026-10-14)[访问日期]. https://allenai.org/blog/hiro-acev2.