
In this tutorial, we build an ensemble weather forecasting workflow with NVIDIA Earth2Studio. We install the required Earth2Studio components while preserving Colab’s existing CUDA-enabled PyTorch environment, load the FCN prognostic model, and retrieve atmospheric initial conditions from GFS. We then implement a custom wind-power diagnostic that converts 10-meter wind components into turbine capacity factors, along with a variable-scaled perturbation system that applies physically appropriate
This tutorial demonstrates how to build an ensemble weather forecasting system using NVIDIA Earth2Studio, which generates multiple weather predictions simultaneously to capture uncertainty. The work involves loading a forecasting model, retrieving atmospheric data, creating custom diagnostics that convert wind measurements into wind-power capacity factors, and applying physically appropriate noise to different atmospheric variables while keeping one unperturbed control forecast. The ensemble forecasts are written to a coordinate-aware data storage system and evaluated against reference analyses using metrics like latitude-weighted RMSE and ensemble spread-skill ratios, with results visualized through spatial maps and forecast skill curves.

While restaurant owners might look to generative AI as a shortcut to sprucing up their menu, customers can viscerally sense that something is wrong with the food.
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ChatGPT, Claude, and Grok all suffered outages at nearly the exact same time for reasons that remain murky.

Most teams building a shopping assistant or agent rebuild the same scaffolding: an agent loop, a tool layer over the catalog, an approval gate, and an eval suite. Anthropic has now released that scaffolding as code. This week, they published anthropics/commerce-agents, a reference blueprint containing a shopping agent and a merchant agent, along with four runnable verticals: retail, travel, telecom and entertainment. It ships alongside two write-ups: a product announcement and an engineering de
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