Datasets:

Emulated-Inc
/

atmoscol

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Atmospheric Column Physics Emulation

Predict the 368 physics tendencies and surface fluxes an embedded high-resolution model produced for an atmospheric column from the column's 556 state variables. Training rows carry inputs and targets in float32 parquet shards; test rows carry inputs only and come from a later, held-out period of the simulation.

The data tree lives under data/, exactly as the benchmark environment presents it at /app/atmoscol. Its own description is at data/README.md.

111 files, 21009196499 bytes (19.6 GiB)
structure hash (sha256 over sorted path\tsize lines): 41bca65b28760379c0976855288b56c64fc7d79f7ea8c512a781d84e15068efc
data.manifest.tsv at the repo root lists every file as sha256 size path

Use

from huggingface_hub import snapshot_download

snapshot_download("Emulated-Inc/atmoscol", repo_type="dataset", revision="REVISION",
                  local_dir="./atmoscol", allow_patterns="data/*")

Pin revision to a commit sha rather than a branch if you need reproducibility.

Provenance and licence

Derived from the LEAP Atmospheric Physics using AI (ClimSim) Kaggle competition (https://www.kaggle.com/competitions/leap-atmospheric-physics-ai-climsim), whose data is released under CC0 1.0 (public domain). The data are outputs of the E3SM-MMF multi-scale climate model; the upstream dataset effort is ClimSim (Yu et al., NeurIPS 2023, arXiv:2306.08754), which the authors ask to be cited. Only the competition's public training data is used; the held-out split here is carved from it.

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Paper for Emulated-Inc/atmoscol

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators

Paper • 2306.08754 • Published Jun 14, 2023 • 3

Atmospheric Column Physics Emulation

Contents

Use

Provenance and licence

Paper for Emulated-Inc/atmoscol