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3D Clouds

This repository hosts the AI-ready datasets for the paper "Global 3D Reconstruction of Clouds & Tropical Cyclones." It contains paired 2D geostationary imagery (GOES, MSG, Himawari) and 3D vertical profiles (CloudSat/CALIPSO) used to train ML models for 3D cloud reconstruction, with a dedicated dataset for tropical cyclones.

Product Details

Visibility: Public
Owner: TACO
Created: 29 Dec 2025
Last Updated: 29 Dec 2025

Product Contents

root

pretraining

goes

README

Cloud 3D - GOES Pretraining Dataset

GOES (Geostationary Operational Environmental Satellite) imagery subset from the Global 3D Cloud Reconstruction Dataset. Contains multispectral geostationary satellite imagery from GOES-16/ABI for 3D cloud structure reconstruction. Each sample contains 20 bands: 16 spectral channels plus satellite and solar angles. 512x512 pixel patches in Cloud-Optimized GeoTIFF format.

Dataset Information

Version: 0.1.0

License: CC-BY-4.0

Keywords: cloud microphysics, 3d reconstruction, geostationary satellites, GOES-16, remote sensing, tropical cyclones, deep learning

Tasks: regression, foundation-model

Dataset Overview

Partitions: 105 files Spatial coverage: [-120.94, -41.13, -1.41, 55.50] (WGS84) Temporal coverage: 2018-01-02 to 2024-12-31

Dataset Structure (Root-Sibling Uniform Tree)

Root: FILE (91,423 samples)

Metadata Fields

LEVEL0

Field	Type	Description
`id`	`string`	Unique sample identifier within parent scope. Must be unique among siblings.
`type`	`string`	Sample type discriminator (FILE or FOLDER).
`taco:header`	`binary`	Binary TACOTIFF header (35 bytes + tile counts) for fast reading without IFD parsing
`stac:crs`	`string`	Coordinate reference system (WKT2, EPSG, or PROJ string)
`stac:tensor_shape`	`list<item: int64>`	Raster dimensions e.g. [bands, height, width]
`stac:geotransform`	`list<item: double>`	GDAL affine transform [origin_x, pixel_width, rot_x, origin_y, rot_y, pixel_height]
`stac:time_start`	`timestamp[us]`	Acquisition start timestamp (microseconds since Unix epoch, UTC)
`stac:centroid`	`binary`	Raster center point in EPSG:4326 (WKB binary format)
`stac:time_end`	`timestamp[us]`	Acquisition end timestamp (microseconds since Unix epoch, UTC)
`stac:time_middle`	`timestamp[us]`	Midpoint between start and end timestamps (microseconds since Unix epoch, UTC)
`geotiff:stats`	`list<item: list<item: float>>`	Per-band statistics (List[List[Float32]]): categorical mode returns class probabilities, continuous mode returns [min, max, mean, std, valid%, p25, p50, p75, p95]
`cloud3d:satellite`	`string`	Geostationary satellite platform (GOES, HIMAWARI, or MSG)
`cloud3d:cyclone`	`bool`	Whether the sample contains tropical cyclone imagery
`majortom:code`	`string`	MajorTOM spherical grid cell identifier (e.g., 0100km_0003U_0005R) with ~dist_km spacing
`geoenrich:elevation`	`float`	Mean elevation in meters (GLO-30 DEM)
`geoenrich:precipitation`	`float`	Mean annual precipitation in mm estimated from GPM data
`geoenrich:temperature`	`float`	Mean annual temperature in °C estimated from MODIS LST data
`geoenrich:admin_countries`	`string`	Country name at centroid location
`internal:current_id`	`int64`	Current sample position at this level (0-indexed). Enables O(1) random access and relational JOINs (ZIP, FOLDER, TACOCAT).
`internal:parent_id`	`int64`	Foreign key referencing parent sample position in previous level (ZIP, FOLDER, TACOCAT).

Usage

Python

R

Julia

Data Providers

NOAA — producer https://www.noaa.gov European Space Agency (ESA) — licensor https://www.esa.int source.coop — host https://source.coop

Dataset Curators

Name	Organization	Email
Cesar Aybar	Universitat de València	cesar.aybar@uv.es
Shirin Ermis	University of Oxford	—
Lilli Freischem	University of Oxford	—
Stella Girtsou	National Observatory of Athens	—
Kyriaki-Margarita Bintsi	Harvard Medical School	—
Emiliano Diaz Salas-Porras	Universitat de València	—
Michael Eisinger	European Space Agency	—
William Jones	University of Oxford	—
Anna Jungbluth	European Space Agency	—
Benoit Tremblay	Environment and Climate Change Canada	—

Publications & Citations

If you use this dataset in your research, please cite:

DOI: 10.48550/arXiv.2511.04773

Ermis, S., Aybar, C., Freischem, L., Girtsou, S., Bintsi, K.-M., Diaz Salas-Porras, E., Eisinger, M., Jones, W., Jungbluth, A., & Tremblay, B. (2025). Global 3D Reconstruction of Clouds & Tropical Cyclones. Tackling Climate Change with Machine Learning Workshop at NeurIPS 2025.

Primary publication describing the dataset and methodology

BibTeX

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