RolnickLab/climart
A benchmark dataset for Machine Learning emulation of atmospheric radiative transfer in weather and climate models (NeurIPS 2021 Datasets and Benchmarks Track)
This dataset and accompanying code help climate scientists and meteorologists accelerate their weather and climate models. It provides comprehensive atmospheric data, including conditions for present, pre-industrial, and future climates. Researchers can use this to train machine learning models that emulate expensive radiative transfer calculations, offering faster predictions for climate simulations.
No commits in the last 6 months.
Use this if you are a climate scientist or atmospheric researcher looking to develop or benchmark machine learning models that can speed up radiative transfer calculations in complex climate simulations.
Not ideal if you need a dataset for general machine learning tasks unrelated to atmospheric science or if you lack the computational resources (GPUs, ample RAM) required for training large models.
Stars
43
Forks
5
Language
Python
License
CC-BY-4.0
Category
Last pushed
Nov 29, 2022
Commits (30d)
0
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