[
    {
        "id": "data:hgpq9-a1861",
        "collection": "data",
        "collection_id": "hgpq9-a1861",
        "cite_using_url": "https://data.caltech.edu/records/hgpq9-a1861",
        "type": "dataset",
        "title": "CloudBench Statistics Dataset",
        "author": [
            {
                "family_name": "Chammas",
                "given_name": "Sheide",
                "orcid": "0000-0001-9001-629X"
            },
            {
                "family_name": "Wang",
                "given_name": "Qing"
            },
            {
                "family_name": "Schneider",
                "given_name": "Tapio",
                "orcid": "0000-0001-5687-2287"
            },
            {
                "family_name": "Carver",
                "given_name": "Rob"
            },
            {
                "family_name": "Shen",
                "given_name": "Zhaoyi",
                "orcid": "0000-0002-0444-4720"
            },
            {
                "family_name": "Parker",
                "given_name": "Jeffrey"
            },
            {
                "family_name": "Gazen",
                "given_name": "Cenk"
            },
            {
                "family_name": "Ihme",
                "given_name": "Matthias"
            },
            {
                "family_name": "Anderson",
                "given_name": "John",
                "orcid": "0009-0000-5341-8919"
            }
        ],
        "abstract": "<h2><strong>CloudBench Dataset</strong></h2><p>This dataset contains the post-processed statistical output from 10,000 Large-Eddy Simulations (LES) driven by a General Circulation Model (GCM), as part of the CloudBench dataset produced by Google Research.</p><p>The data was generated by performing an ensemble of high-resolution large-eddy simulations (LES) covering diverse meteorological conditions across the tropical Pacific. The LES are forced with the large-scale conditions derived from a single GCM model (the GFDL CM4) for 500 locations, 4 representative months (January, April, July, and October), and 5 different climate scenarios.</p><p>The simulations were conducted in Swirl-LM, a computational fluid dynamics (CFD) simulation framework that is accelerated by the Tensor Processing Unit (TPU). Refer to the documentation in <a href=\"https://github.com/google-research/swirl-lm\">Swirl-LM</a> for information about installation, required hardware, and instructions on how to run large simulations on TPU nodes in Google Cloud.<br><br>Further details on the dataset are available in the <a href=\"https://github.com/google-research/swirl-lm/tree/main/swirl_lm/example/geo_flows/cloud_feedback/README.md\">README</a> file.</p>",
        "doi": "10.22002/hgpq9-a1861",
        "publisher": "CaltechDATA",
        "publication_date": "2026-04-09"
    },
    {
        "id": "data:z24s9-nqc90",
        "collection": "data",
        "collection_id": "z24s9-nqc90",
        "cite_using_url": "https://data.caltech.edu/records/z24s9-nqc90",
        "type": "dataset",
        "title": "Error analysis of climate models from CMIP3 through CMIP6, including AMIP and higher-resolution models",
        "author": [
            {
                "family_name": "Wills",
                "given_name": "Robert C. J.",
                "orcid": "0000-0002-7776-2076"
            },
            {
                "family_name": "Schneider",
                "given_name": "Tapio",
                "orcid": "0000-0001-5687-2287"
            }
        ],
        "abstract": "<p>This archive contains observations and climate model output, including Matlab analysis scripts, to produce Figure 1 in Schneider, Leung, and Wills, \"Optimizing climate models with process-knowledge, resolution, and AI,\" published in Atmospheric Chemistry and Physics.&nbsp;</p>",
        "doi": "10.22002/z24s9-nqc90",
        "publisher": "CaltechDATA",
        "publication_date": "2024-04-09"
    },
    {
        "id": "data:qemqk-rgq45",
        "collection": "data",
        "collection_id": "qemqk-rgq45",
        "cite_using_url": "https://data.caltech.edu/records/qemqk-rgq45",
        "type": "software",
        "title": "Atmospheric spectra plots from \"Opinion: Optimizing climate models with process-knowledge, resolution, and AI\", Atmos. Chem. Phys. 2024",
        "author": [
            {
                "family_name": "Schneider",
                "given_name": "Tapio",
                "orcid": "0000-0001-5687-2287"
            }
        ],
        "abstract": "<p>This repository contains code to plot the atmospheric spectra in Figure 3 in&nbsp;</p><p>T. Schneider, L. R. Leung, and R. C. J. Wills. Opinion: Optimizing climate models with process- knowledge, resolution, and AI. Atmos. Chem. Phys., in review, 2024.</p><p>It relies on data provided by others, included as a courtesy here.&nbsp;</p>",
        "doi": "10.22002/qemqk-rgq45",
        "publisher": "CaltechDATA",
        "publication_date": "2024-03-31"
    },
    {
        "id": "data:ywa3k-94m65",
        "collection": "data",
        "collection_id": "ywa3k-94m65",
        "cite_using_url": "https://data.caltech.edu/records/ywa3k-94m65",
        "type": "dataset",
        "title": "Data for \"Top-of-atmosphere albedo bias from neglecting three-dimensional cloud radiative effects''",
        "author": [
            {
                "family_name": "Singer",
                "given_name": "Clare E.",
                "orcid": "0000-0002-1708-0997"
            },
            {
                "family_name": "Lopez-Gomez",
                "given_name": "Ignacio",
                "orcid": "0000-0002-7255-5895"
            },
            {
                "family_name": "Zhang",
                "given_name": "Xiyue",
                "orcid": "0000-0002-6031-7830"
            },
            {
                "family_name": "Schneider",
                "given_name": "Tapio",
                "orcid": "0000-0001-5687-2287"
            }
        ],
        "abstract": "This dataset contains 3D fields from the PyCLES large-eddy simulation model post-processed into the format required for the libRadtran MYSTIC radiative transfer solver. These data were used in the paper \"Top-of-atmosphere albedo bias from neglecting three-dimensional cloud radiative effects''. Also included are the uvspec.template files required by libRadtran.",
        "doi": "10.22002/D1.1637",
        "publisher": "CaltechDATA",
        "publication_date": "2020-09-30"
    }
]