LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorologicall Reanalysis Data

Citation Author(s):
Cong
Bai
Zhipeng
Cai
Xiaomei
Yin
Jinglin
Zhang
Submitted by:
zhipeng cai
Last updated:
Tue, 10/31/2023 - 04:41
DOI:
10.21227/g8x0-1744
Data Format:
HDF5
Research Article Link:
LSDSSIMR: Large-Scale Dust Storm Database Based on Satellite Images and Meteorologicall Reanalysis Data
License:
Creative Commons Attribution
742 Views
Categories:
Artificial Intelligence
Machine Learning
Geo-Sensing
Computer Vision
Climate Change/Environmental
Weather
Geoscience and Remote Sensing
Remote Sensing
Keywords:
Dust storm; Database; FY-4A; Meteorological reanalysis; Remote sensing; Geoscience;
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Abstract 

Since meteorological satellites can observe the Earth’s atmosphere from a spatial perspective at a large scale, in this paper, a dust storm database is constructed using multi-channel and dust label data from the Fengyun-4A (FY-4A) geosynchronous orbiting satellite, namely, the Large-Scale Dust Storm database based on Satellite Images and Meteorological Reanalysis data (LSDSSIMR), with a temporal resolution of 15 minutes and a spatial resolution of 4 km from March to May of each year during 2020–2022. Meteorological reanalysis data are added to LSDSSIMR for spatiotemporal prediction methods. Each data file is stored in HDF5 format, and the final LSDSSIMR contains nearly 10,000 HDF5 files. Moreover, some traditional dust detection methods based on spectral analysis are executed as a benchmark.

Instructions: 

LSDSSIMR is a database for dust storms from March to May during 2020–2022, with a temporal resolution of 15 minutes and a spatial resolution of 4 km. The data are saved as HDF5 format files. Each HDF5 file contains satellite multichannel observation Level 1 data and dust detection scoring Level 2 data. The results of traditional spectral analysis dust detection methods such as brightness temperature adjusted dust index (BADI), infrared difference dust index (IDDI), and normalized difference dust index (NDDI) are added to the LSDSSIMR, which can be used as a benchmark for dust detection in the future. In addition, 5 the LSDSSIMR includes hourly atmospheric reanalysis data from MERRA-2 and ERA-5. Based on these data, the LSDSSIMR can be used as a database for detecting, monitoring, and predicting dust storms.

Funding Agency: 
Zhejiang Provincial Natural Science Foundation of China; Major Basic Research Projects of Shandong Province; Distinguished Young Scholar of Shandong Province; Taishan Scholars Program;
Grant Number: 
LR21F020002; ZR2022ZD32; ZR2023JQ025; tsqn202211290;
Data Descriptor Article DOI: 
https://doi.org/10.1109/JSTARS.2023.3325783

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