A global process-oriented sea surface temperature anomaly dataset retrieved from remote sensing products

Figures & data

Table 1. Main SST datasets derived from remote-sensing data.

	Source/Sensor	Spatial resolution	Period covered	Temporal resolution
1	AVHRR Pathfinder SST v5.3 (Saha et al., 2018)	4 km	1981.01–2020.12	Sub-daily
2	AMSR-E (Wentz, Meissner, Gentemann, & Brewer, 2014)	0.25°	2002.06–2011.10	Daily/Monthly/Weekly
3	NOAA OI SST V2 (Reynolds et al., 2002)	1°	1981.12–2020.12	Monthly/Weekly
4	NOAA OI SST V2 High Resolution Dataset (Reynolds et al., 2007)	0.25°	1981.09–2020.12	Daily
5	MODIS-Aqua L3SMI^a	4 km	2002.07–2020.12	Daily/Monthly
6	FY2C VIRR daily sea surface temperature^b	0.05°	2014.05.14–2020.12.31	Daily
7	MERRA-2^c	0.5° × 0.625°	1980.01–2021.12	Monthly
8	ERA-5^d	0.281°	2008.01–2017.12	Monthly

^ahttps://oceandata.sci.gsfc.nasa.gov/MODIS-Aqua/L3SMI/.

^bhttp://satellite.nsmc.org.cn/portalsite/default.aspx.

^cGlobal Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavgM_2d_ocn_Nx: 2d, Monthly mean, Time-Averaged, Single-Level, Assimilation, Ocean Surface Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 08/11/2021, 10.5067/4IASLIDL8EEC.

^dEuropean Centre for Medium-Range Weather Forecasts. 2017, updated monthly. ERA5 Reanalysis Monthly Means. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. 10.5065/D63B5XW1. Accessed on 08/11/2021.

Figure 1. Diagram showing an example from GDPoSSTA.

Figure 2. Workflow used to produce GDPoSSTA.

Figure 3. Spatial distribution of the 87 POSSTAs in the global ocean for the period January 1982 to December 2009. Different colors represent different POSSTAs.

Table 2. Fields in ProcessObjectDatasets.shp.

Name	Definition	Comment
FID	LONG	Feature identifier, default field of SHP format
Shape	Polygon	A polygon, default field of SHP format
POID	LONG	POSSTA object identifier
STime	STRING	Time that the POSSTA begins to form
ETime	STRING	Time that the POSSTA dissipates
DurTime	INT	Duration of the POSSTA (units: months)
AvgValue	DOUBLE	Monthly average SSTA value of the POSSTA (units: degree Celsius)
MinValue	DOUBLE	Minimum SSTA value of the POSSTA (units: degree Celsius)
MaxValue	DOUBLE	Maximum SSTA value of the POSSTA (units: degree Celsius)
MinLon	DOUBLE	Minimum longitude of the POSSTA (units: degrees)
MinLat	DOUBLE	Minimum latitude of the POSSTA (units: degrees)
MaxLon	DOUBLE	Maximum longitude of the POSSTA (units: degrees)
MaxLat	DOUBLE	Maximum latitude of the POSSTA (units: degrees)
Abnormal	STRING	An abnormal type of VOSSTA; i.e. a warm or a cold anomaly

Table 3. Fields in SequenceObjectDatasets.shp.

Name	Definition	Comment
FID	LONG	Feature identifier, default field of SHP format
Shape	Polygon	A polygon, default field of SHP format
POID	LONG	As for Table 2; used to associate a POSSTA with SOSSTAs
SQID	STRING	Unique SOSSTA identifier
STime	STRING	Time that the SOSSTA begins to form
ETime	STRING	Time that the SOSSTA ends
DurTime	INT	Duration of the SOSSTA (units: months)
AvgValue	DOUBLE	Monthly average SSTA value of the SOSSTA (units: degree Celsius)
MinValue	DOUBLE	Minimum SSTA value of the SOSSTA (units: degree Celsius)
MaxValue	DOUBLE	Maximum SSTA value of the SOSSTA (units: degree Celsius)
MinLon	DOUBLE	Minimum longitude of the SOSSTA (units: degrees)
MinLat	DOUBLE	Maximum longitude of the SOSSTA (units: degrees)
MaxLon	DOUBLE	Maximum longitude of the SOSSTA (units: degrees)
MaxLat	DOUBLE	Maximum latitude of the SOSSTA (units: degrees)
SeqType	STRING	The type of SOSSTA; i.e. production, development or dissipation sequence (Xue et al., 2019a).
Theta	DOUBLE	The direction of movement of the SOSSTA expressed as an angle measured counterclockwise from a horizontal to the right (range: 0–360°).
Abnormal	STRING	An abnormal type of a VOSSTA; i.e. a warm or a cold anomaly.

Table 4. Fields in VariationObjectDatasets.shp.

Name	Definition	Comment
FID	LONG	Feature identifier, default field of SHP format
Shape	Polygon	A polygon, default field of SHP format
POID	LONG	As for Table 2; used to associate a POSSTA with VOSSTAs
SQID	STRING	As for Table 3; used to associate a SOSSTA with VOSSTAs
STID	STRING	Unique VOSSTA identifier
Time	STRING	Time of occurrence of the VOSSTA
Area	DOUBLE	Area covered by the VOSSTA
AvgValue	DOUBLE	SSTA value of the VOSSTA (units: degree Celsius)
MinValue	DOUBLE	Minimum SSTA value within the area covered by the VOSSTA (units: degree Celsius)
MaxValue	DOUBLE	Maximum SSTA value within the area covered by the VOSSTA (units: degree Celsius)
MinLon	DOUBLE	Minimum longitude of the area covered by the VOSSTA (units: degrees)
MinLat	DOUBLE	Minimum latitude of the area covered by the VOSSTA (units: degrees)
MaxLon	DOUBLE	Maximum longitude of the area covered by the VOSSTA (units: degrees)
MaxLat	DOUBLE	Maximum latitude of the area covered by the VOSSTA (units: degrees)
CoreLon	DOUBLE	Central longitude of the area covered by the VOSSTA (units: degrees)
CoreLat	DOUBLE	Central latitude of the area covered by the VOSSTA (units: degrees)
Abnormal	STRING	An abnormal type of a VOSSTA; i.e. a warm or a cold anomaly

Table 5. Fields in SequenceRelationship.csv.

Name	Definition	Comment
SQID_1	STRING	SOSSTA 1 object identifier
SQID_2	STRING	SOSSTA 2 object identifier
Relationship	STRING	Type of evolving behavior between the two SOSSTAs; i.e. development, splitting, merging or splitting–merging
Abnormal	STRING	An abnormal type of a VOSSTA; i.e. a warm or a cold anomaly

Table 6. Fields in ObjectRelationship.csv.

Name	Definition	Comment
PRIOR_STID	STRING	SOSSTA object identifier at previous time snapshot
NEXT_STID	STRING	SOSSTA object identifier at next time snapshot
Relationship	STRING	Type of evolving behavior between the two VOSSTAs; i.e. development, splitting, merging or splitting–merging
Abnormal	STRING	An abnormal type of a VOSSTA; i.e. a warm or a cold anomaly

Table 7. Details of the files contained in the ScienceDB record.

No	File List	Description
1	ProcessObjectDatasets.dbf	ArgGIS SHP files in the ProcessObjectDatasets
2	ProcessObjectDatasets.prj
3	ProcessObjectDatasets.sbn
4	ProcessObjectDatasets.sbx
5	ProcessObjectDatasets.shp
6	ProcessObjectDatasets.shp.xml
7	ProcessObjectDatasets.shx
8	SequenceObjectDatasets.dbf	ArgGIS SHP file in the SequenceObjectDatasets
9	SequenceObjectDatasets.prj
10	SequenceObjectDatasets.sbn
11	SequenceObjectDatasets.sbx
12	SequenceObjectDatasets.shp
13	SequenceObjectDatasets.shp.xml
14	SequenceObjectDatasets.shx
15	VariationObjectDatasets.dbf	ArgGIS SHP files in the VariationObjectDatasets
16	VariationObjectDatasets.prj
17	VariationObjectDatasets.sbn
18	VariationObjectDatasets.sbx
19	VariationObjectDatasets.shp
20	VariationObjectDatasets.shp.xml
21	VariationObjectDatasets.shx
22	ObjectRelationship.csv	Relationship files in which the evolving behaviors between VOSSSTAs are stored
23	SequenceRelationship.csv	Relationships files in which the evolving behaviors between SOSSTAs are stored

Figure 4. Comparison of the spatial distribution of VOSSTAs identified using the proposed DcSTCA and k-Standard Deviation. (a) VOSSTAs retrieved from an SST dataset from March 1983 using the proposed algorithm DsSTCA. (b) Comparison of VOSSTAs identified by the k-Standard Deviation algorithm and the proposed algorithm for k equals 1 and 2 (zoomed in part of (a)).

Table 8. Comparison of the tracking of process objects by the proposed PoAIR algorithm and TITAN (Modified from Figure 9 and Table 3 in Xue et al. (2019b)).

	Process object 1				Process object 2				Process object 3				Process object 4
	PoAIR		TITAN		PoAIR		TITAN		PoAIR		TITAN		PoAIR		TITAN
NTO	10				22				19				17
NDO	6	4	6	4	20		12+1	9	18		15+1		17		10+2	7
POD (%)	60.00		60.00		90.91		54.54		94.70		78.95		100.00		58.82
	Process object 5				Process object 6				Process object 7
	PoAIR		TITAN		PoAIR		TITAN		PoAIR		TITAN
NTO	11				22				19
NDO	11		18+1	3	20	2       9	9        9	2	19	11	6	2
POD (%)	100.00		72.72		90.91		45		100		57.89
	Process object 8				Process object 9				Process object 10
	PoAIR		TITAN		PoAIR		TITAN		PoAIR		TITAN
NTO	25				8				18
NDO	25		12       8	2       2	5	3	5	3	18		12+1	6+1
POD (%)	100.00		48.00		62.50		62.50		100.00		66.67

Table 9. Comparison of identified evolving behaviors by the proposed PoAIR algorithm and TITAN.

	Developing		Merging		Splitting		Splitting–merging
	PoAIR	TITAN	PoAIR	TITAN	PoAIR	TITAN	PoAIR	TITAN
NTE	109		18		22		14
NDE	100	81	16	8	16	12	12	6
POD (%)	91.74	74.31	88.89	44.44	72.73	54.56	85.71	42.86

Figure 5. The POSSTA that was selected for analysis. The black polygons denote VOSSTAs; the arrows denote the evolution of the VOSSTAs. A total of six evolutionary sequences are shown by the six colored lines. The background is the monthly SSTA.

Figure 6. Graph showing the close relationship between the selected POSSTA and an ENSO event.

Saha, K.…, Zhao, K., Xuepeng, X., Zhang, H.-M., Casey, K. S., Zhang, D., Sheekela, B-Y.,Kilpatrick, K.D., Evans, R.H., Ryan, T., Relph, J.M. (2018). AVHRR Pathfinder version 5.3 level 3 collated (L3C) global 4km sea surface temperature for 1981-Present. NOAA National Centers for Environmental Information. Dataset. doi: https://doi.org/10.7289/v52j68xx.

 Google Scholar

Wentz, F. J. T., Meer, C. T., Gentemann, M. C., & Brewer, M. (2014). Remote sensing systems AQUA AMSR-E daily, weekly, and monthly. Environmental suite on 0.25 deg grid, Version 8.2. Remote Sensing Systems. Santa Rosa, CA. Retrieved from www.remss.com/missions/amsr

 Google Scholar

Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., & Wang, W. Q. (2002). An improved in situ and satellite SST analysis for climate. Journal of Climate, 15(13), 1609–1625.

 Web of Science ®Google Scholar

Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., & Schlax, M. G. (2007). Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22), 5473–5496.

 Web of Science ®Google Scholar

Xue, C. J., Wu, C. B., Liu, J. Y., & Su, F. Z. (2019a). A novel process-oriented graph storage for dynamic geographic phenomena. ISPRS International Journal of Geo-Information, 8(2), 100.

 Web of Science ®Google Scholar

Xue, C. J., Liu, J. Y., Yang, G., & Wu, C. (2019b). A process-oriented method for tracking rainstorms with a time-series of raster datasets. Applied Sciences, 9(12), 2468.

 Google Scholar

Data Availability Statement

The data that support the findings of this study are openly available in ScienceDB at http://www.doi.org/10.11922/sciencedb.j00076.00090.