Digital Typhoon Dataset


Citation for this dataset
Asanobu KITAMOTO. (2023). Digital Typhoon Dataset [Data set]. Data Integration and Analysis System (DIAS). https://doi.org/10.20783/DIAS.664
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IDENTIFICATION INFORMATION

Name Digital Typhoon Dataset
DOI doi:10.20783/DIAS.664
Metadata Identifier DigitalTyphoon20231105235443-DIAS20221121113753-en

CONTACT

CONTACT on DATASET

Name Asanobu KITAMOTO
Organization National Institute of Informatics
Address 2-1-2, Hitotsubashi, Chiyoda-ku, Tokyo, 101-8430, Japan
E-mail kitamoto@nii.ac.jp

CONTACT on PROJECT

Data Integration and Analysis System

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
E-mail dias-office@diasjp.net

DOCUMENT AUTHOR

Name Asanobu KITAMOTO
Organization National Institute of Informatics
E-mail kitamoto@nii.ac.jp

DATASET CREATOR

Name Asanobu KITAMOTO
Organization National Institute of Informatics
E-mail kitamoto@nii.ac.jp

DATE OF THIS DOCUMENT

2023-11-05

DATE OF DATASET

  • creation : 2023-11-05

DATASET OVERVIEW

Abstract

The "Digital Typhoon Dataset" is a dataset of meteorological satellite images and the best track of typhoons in the Northwest Pacific basin. The meteorological satellite imagery is created from infrared imagery (11 micrometers) from geostationary meteorological satellites Himawari 1 to 9 by a map projection (Lambert azimuthal equal-area) of a 1250 km radius centered on the typhoon. The best track is created from the best track data published by the Japan Meteorological Agency by interpolating it into hourly data. As of October 2023, it consists of 189364 images for 1099 typhoons from 1978 to 2022 and will be updated annually. This is a particularly useful dataset for machine learning, and the official dataset page explains its use for machine learning.

Topic Category(ISO19139)

  • climatologyMeteorologyAtmosphere

Temporal Extent

Begin Date 1978
End Date Under Continuation
Temporal Characteristics Hourly

Geographic Bounding Box

North bound latitude 70
West bound longitude 100
Eastbound longitude 180
South bound latitude 0

Keywords

Keywords on Dataset

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

Keywords on Project

Data Integration and Analysis System
Keyword Type Keyword Keyword thesaurus Name
theme DIAS > Data Integration and Analysis System No_Dictionary

Online Resource

Distribution Information

name version specification
HDF5 5
CSV RFC 4180

DATA POLICY

Data Policy by the Project

Data Integration and Analysis System

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.

DATA SOURCE ACKNOWLEDGEMENT

Acknowledge the Data Provider

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

Acknowledge the Project

Data Integration and Analysis System

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. "

REFERENCES

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

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