A global process-oriented sea surface temperature anomaly dataset retrieved from remote sensing products
From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-01-01
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| Series: | Big Earth Data |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/20964471.2021.1988426 |
| Summary: | From the time that it first develops, a sea surface temperature anomaly (SSTA) will develop in space and time until it dissipates. Although many SST products are available, great challenges are still faced when attempting to directly explore the evolution of SSTAs. To address some of these problems, in this study, we developed a global SSTA dataset that included details of the spatial structure of SSTAs and their temporal evolution. This dataset is called GDPoSSTA. GDPoSSTA is comprised of three datasets and two relationship files and covers the period from January 1982 to December 2009. The three datasets are in SHP format and consist of a dataset of processed object-oriented SSTAs named DSPOSSTA, a dataset of sequenced object-oriented SSTA series named DSSOSSTA, and a dataset of variation object-oriented SSTA named DSVOSSTA. The two relationship files, which are in CSV format, store the evolving behavior of the SSTA sequence object and SSTA variation objects. Finally, geographic spatiotemporal statistics are derived for the DSPOSSTA and a comparison of applying TITAN to DSVOSSTA and DSPOSSTA is carried out which demonstrates the feasibility and applicability of GDPoSSTA. The GDPoSSTA dataset is available on ScienceDB platform (http://www.doi.org/10.11922/sciencedb.j00076.00090). |
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| ISSN: | 2096-4471 2574-5417 |