**WeatherNext** is Google DeepMind's open repository for its global weather forecasting models, and it picked up **670 stars this week** to reach **7,524 stars and 961 forks**. The spike has a specific cause: release **v0.3.0 on 6 August 2026** added support for **WeatherNext 2**, published the same day as the DeepMind blog post on cyclone forecasting and a **Nature** paper on operational tropical cyclone forecasting with AI. The repository itself is not new — it was created in **July 2023** as `google-deepmind/graphcast` and later renamed, which is why the pretrained weights still live in a Google Cloud bucket called `dm_graphcast`. The results are the reason to look. On cyclones, DeepMind reports that **three-day forecasts are as good as what prior models provided for two days** — described as more than a full 24 hours of lead-time advantage, and framed in the blog as roughly a decade's worth of meteorological progress. The blog benchmarks track against **ECMWF-ENS** and intensity against **HWRF** over 2023-2025, reporting a lead advantage on both. The model produces **1,000 possible scenarios per cyclone**, and during the 2025 hurricane season DeepMind says it helped the **National Hurricane Center** on **Hurricane Melissa**, including its rapid intensification and Jamaica landfall. Partners named include the NHC, **CIRA**, and the **UK Met Office**. WeatherNext 2 itself was announced on **17 November 2025** and is built on a **Functional Generative Network (FGN)**, an architecture that injects noise directly into the model so forecasts stay physically realistic and internally consistent rather than averaging into mush. It covers **0 to 15 days** at up to **1-hour increments**, generates predictions **8x faster** than the previous WeatherNext, takes **under a minute on a single TPU**, and DeepMind reports it surpasses the prior model on **99.9% of variables and lead times**. It already powers weather in **Search, Gemini, Pixel Weather, and the Google Maps Platform Weather API**. What this repository actually gives you is the runnable version of that. Weights ship for **WeatherNext2_<2025** (0.25°, roughly 30 km, fine-tuned on ECMWF HRES and designed to initialise from operational HRES conditions rather than ERA5 reanalysis), plus **WeatherNextCyclones** checkpoints for <2025, <2024, and <2023 that reproduce the paper's per-year results, each as four model files. Crucially there is also **WeatherNextCyclones Mini** at **1° resolution (about 111 km)** — a deliberately smaller model that forecasts the same fields, including cyclones, on a single accelerator. The Colab demo defaults to Mini on the free **v5e-1** TPU runtime and walks through loading weights, running autoregressive rollouts, visualising temperature and wind, running the cyclone tracker, and taking a gradient step. The legacy **GraphCast** and **GenCast** models remain in the repo as WeatherNext Graph and WeatherNext Gen. The limits are stated plainly by Google and deserve repeating. This is **research code provided as-is**, with **no API stability** and breaking changes possible without notice — the README recommends pinning to a release. Hardware is a real gate: the non-Mini models need an **H100** for sufficient VRAM, TPU is the optimised target, and running on GPU requires switching the attention implementation. Training from scratch means pulling **ERA5** and **HRES** data from ECMWF under their own terms. Licensing is split — Apache-2.0 for code and notebooks, **CC BY 4.0** for everything else. And Google is explicit that this is **not an officially supported Google product**, has not been produced with or endorsed by any government meteorological agency, and does not replace official alerts or warnings.