Datasets:

tacofoundation
/

methaneset

Synthetic methane plume bank generated from WRF-LES 4.7.0 atmospheric simulations (Gorroño et al., Zenodo doi:10.5281/zenodo.18161182). Contains 1,064,448 ΔXCH4 maps [mol/mol] at 20m resolution spanning 11 wind speeds, 12 wind directions, 9 multisource and 5 areasource plume geometries, 3 temporal snapshots, 8 Solar Zenith Angles, and 24 Solar Azimuth Angles. All plumes are normalized to a reference emission rate of 3000 kg/h and scale linearly. Intended for synthetic data augmentation in satellite-based methane plume detection models.

Dataset Information

Version: 2.0.0

License: CC-BY-4.0

Keywords: methane, plume-detection, synthetic-data, WRF-LES, Sentinel-2, Landsat, EMIT, remote-sensing, earth-observation, data-augmentation, greenhouse-gas

Tasks: regression, segmentation

Dataset Overview

Partitions: 8 files Spatial coverage: [-180.00, -90.00, 180.00, 90.00] (WGS84)

Dataset Structure (Root-Sibling Uniform Tree)

Root: FILE (1,064,448 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).
`simulation:source_type`	`string`	Simulation type: 'multi' (point sources) or 'area' (distributed)
`simulation:wind_speed`	`float`	Geostrophic wind speed [m/s]
`simulation:wind_dir`	`int16`	Wind direction [degrees, 0=North]
`simulation:plume_id`	`int8`	Plume identifier (multi: 1-9, area: 1-5)
`simulation:snapshot`	`int16`	WRF-LES snapshot index (70, 90, or 110)
`simulation:sza`	`int8`	Solar Zenith Angle [degrees]
`simulation:saa`	`int16`	Solar Azimuth Angle [degrees, 0=North]
`simulation:q_ref`	`float`	Reference emission rate [kg/h] — scale linearly for other rates
`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

# pip install tacoreader
import tacoreader

ds = tacoreader.load("methane-plume-bank.tacozip")
print(f"ID: {ds.id}")
print(f"Version: {ds.version}")
print(f"Samples: {len(ds.data)}")

R

# Coming soon: R support is planned but not yet available
# install.packages("tacoreader")
library(tacoreader)

ds <- load_taco("methane-plume-bank.tacozip")
cat(sprintf("ID: %s\n", ds$id))
cat(sprintf("Version: %s\n", ds$version))
cat(sprintf("Samples: %d\n", nrow(ds$data)))

Julia

# Coming soon: Julia support is planned but not yet available
# using Pkg; Pkg.add("TacoReader")
using TacoReader

ds = load_taco("methane-plume-bank.tacozip")
println("ID: ", ds.id)
println("Version: ", ds.version)
println("Samples: ", size(ds.data, 1))

Data Providers

Image and Signal Processing Group (ISP-UV) — producer

LARS-UPV — producer

Dataset Curators

Name	Organization	Email
Cesar Aybar	Image and Signal Processing Group, University of Valencia	csaybar@uv.es

Publications & Citations

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

DOI: 10.5281/zenodo.18161182

Gorroño, J., Pei, Z., & Guanter, L. (2025). Benchmark simulations for methane emissions validation and sensitivity studies. Zenodo.

Source WRF-LES simulations used to generate the plume bank.

DOI: 10.5194/egusphere-2025-4924

Gorroño, J., et al. (2025). Considering the observation and illumination angular configuration for improved methane detection. EGUsphere.

Describes the parallax correction methodology applied during projection.

BibTeX

@dataset{methane-plume-bank2,
  title = {MethanePlumeBank: Synthetic Methane Plume Bank from WRF-LES Simulations},
  author = {Cesar Aybar},
  year = {2024},
  version = {2.0.0},
  publisher = {Image and Signal Processing Group, University of Valencia}
}

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