Building Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio

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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 noise amplitudes to different atmospheric variables while retaining an unperturbed control member. Using Earth2Studio’s low-level iterator, coordinate-mapping, batching, and Zarr APIs, we construct our own ensemble execution pipeline, write forecast and diagnostic fields to a coordinate-aware data store, and verify the forecasts against GFS analyses using latitude-weighted RMSE, fair CRPS, ensemble spread, and spread-skill ratios. Finally, we visualize ensemble uncertainty through spatial maps, geopotential-height spaghetti contours, point-based fan charts, wind-capacity-factor forecasts, and lead-time skill curves.

Copy CodeCopiedUse a different Browserimport importlib.util, os, subprocess, sys if importlib.util.find_spec("earth2studio") is None: import numpy as _np, torch as _torch cfile = os.path.join(os.getcwd(), "e2s_constraints.txt") with open(cfile, "w") as f: f.write(f"torch=={_torch.__version__.split('+')[0]}\n") f.write(f"numpy=={_np.__version__}\n") env = {**os.environ, "PIP_CONSTRAINT": cfile} subprocess.check_call( [sys.executable, "-m", "pip", "install", "-q", …

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