| |
| Name | Digital Typhoon Dataset |
| Edition | V3-2025.R2 |
| DOI | doi:10.20783/DIAS.664 |
| Metadata Identifier | DigitalTyphoon20261031193551-DIAS20221121113753-en |
| Name | Asanobu KITAMOTO |
|---|---|
| Organization | National Institute of Informatics |
| Address | 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan |
| kitamoto@nii.ac.jp |
| Name | DIAS Office |
|---|---|
| Organization | Japan Agency for Marine-Earth Science and Technology |
| Address | 3173-25, Showa-Cho, Kanazawa-ku, Yokohama-shi, Kanagawa, 236-0001, Japan |
| dias-office@diasjp.net |
| Name | Asanobu KITAMOTO |
|---|---|
| Organization | National Institute of Informatics |
| kitamoto@nii.ac.jp |
| Name | Asanobu KITAMOTO |
|---|---|
| Organization | National Institute of Informatics |
| kitamoto@nii.ac.jp |
creation : 2023-11-05
revision : 2026-10-16
The "Digital Typhoon Dataset" is a meteorological satellite image and best-track dataset for typhoons in the Northern Hemisphere's Northwest Pacific (WP) and cyclones around the Southern Hemisphere's Australia (AU).
The meteorological satellite images are map-projected (using an azimuthal equidistant projection) within a 1250 km radius centered on the typhoon, utilizing multispectral images (Channels 1–4) from geostationary meteorological satellites Himawari-1 through 9. The best-track data is created by interpolating the best-track data published by the Japan Meteorological Agency (JMA) into hourly data.
As of October 2026, the latest version is V3-2025.R2, comprising 818,881 images for 1,680 typhoons (cyclones) from 1978 to 2025 seasons. This dataset is useful for machine learning, and the official dataset page also explains its applications in machine learning.
Change Log:
Version 1 to Version 2:
* Added data for the Southern Hemisphere (around Australia).
* Changed the map projection from an azimuthal equal-area projection to an azimuthal equidistant projection.
* Extended the time coverage up to the 2023/2024 season.
Version 2 to Version 3 (V3-2025.R2)
* Added data for Channels 2–4.
* Extended the temporal coverage up to the 2025 season.
* Fixed remaining data bugs present in V1/V2.
climatologyMeteorologyAtmosphere
| Begin Date | 1978 |
| End Date | 2025 |
| Temporal Characteristics | Hourly |
| North bound latitude | 70 |
| West bound longitude | 100 |
| Eastbound longitude | 180 |
| South bound latitude | -70 |
| Keyword Type | Keyword | Keyword thesaurus Name |
|---|---|---|
| theme | ATMOSPHERIC PROCESSES > Tropical meteorology | AGU |
| theme | Earth Observation Satellites > GEOSTATIONARY SATELLITES | GCMD_platform |
| theme | Weather, Disasters, Climate | GEOSS |
| discipline | machine learning | No_Dictionary |
| Keyword Type | Keyword | Keyword thesaurus Name |
|---|---|---|
| theme | DIAS > Data Integration and Analysis System | No_Dictionary |
Official page of the "Digital Typhoon Dataset" : https://agora.ex.nii.ac.jp/digital-typhoon/dataset/
File download : https://data.diasjp.net/dl/storages/filelist/dataset:664
| name | version | specification |
|---|---|---|
| HDF5 | 5 | |
| CSV | RFC 4180 |
If data provider does not have data policy, DIAS Terms of Service (https://diasjp.net/en/terms/) and DIAS Privacy Policy (https://diasjp.net/en/privacy/) apply.
If there is a conflict between DIAS Terms of Service and data provider's policy, the data provider's policy shall prevail.
CC-BY 4.0 :
Creative Commons Attribution 4.0 International
The attribution of the dataset required for the CC BY license is as follows.
Digital Typhoon dataset (National Institute of Informatics) doi: https://doi.org/10.20783/DIAS.664
If you plan to use this dataset for a conference presentation, paper, journal article, or report etc., please include acknowledgments referred to following examples. If the data provider describes examples of acknowledgments, include them as well.
" In this study, [Name of Dataset] provided by [Name of Data Provider] was utilized. This dataset was also collected and provided under the Data Integration and Analysis System (DIAS), which was developed and operated by a project supported by the Ministry of Education, Culture, Sports, Science and Technology. "
Asanobu Kitamoto, Victor Le Louarn, Noelia Falczuk, Ngoc Anh Tong, Anja Veleva, "Digital Typhoon Dataset V3: Benchmarking for Multi-spectral Extension and Analyzing Systematic Bias in Intensity Regression", arXiv:2610.21654, 2026.
Asanobu Kitamoto, Erwan Dzik, Gaspar Faure, "Machine Learning for the Digital Typhoon Dataset: Extensions to Multiple Basins and New Developments in Representations and Tasks", arXiv:2411.16421, 2024.
Asanobu KITAMOTO, Jared HWANG, Bastien VUILLOD, Lucas GAUTIER, Yingtao TIAN, Tarin CLANUWAT, "Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones", NeurIPS 2023 Datasets and Benchmarks (Spotlight), 2023
