Superficial sea temperature trend map at low spatial resolution (~5km)

The superficial sea temperature (SST) map package was built with daily-mean data at low spatial resolution (0.05 degree) remotely collected between 1982 and 2020. The maps were also built over the four quarters of the time period. These layers are provided with the suffixes Q1 to Q4. The SST trend maps show the increasing or decreasing trend of SST over the selected time period.

Date ( Creation ): 2021-02-23

Identifier: https://geonetwork.bioinfo.szn.it/geonetwork/srv/eng/catalog.search#/metadata/cc3005aa-bff1-4623-b5ce-2547f4cbc551

Credit: SZN

Status: Completed

Resource provider

Stazione Zoologica Anton Dohrn - Salvatore Valente ( )

Maintenance and update frequency: As needed

Keywords

feamp
campania
temperature

GEMET - INSPIRE themes, version 1.0 GEMET - INSPIRE themes, version 1.0 ( Theme )

Hydrography

Use limitation: None

Use constraints: Other restrictions

Other constraints: Data policy of the project (link)

Access constraints: Other restrictions

Other constraints: Data policy of the project (link)

Classification: Unclassified

Spatial representation type: Grid

Metadata language: it

Character set: UTF8

Topic category

Oceans

Supplemental Information: Any other descriptive information about the dataset

Reference system identifier: WGS 1984

Spatial representation info

No information provided.

Number of dimensions: 3

Cell geometry: Area

Distribution format

GeoTiff ( 1.0 )

Distributor

Stazione Zoologica Antonio Dohrn - Marilù Chiusano ( BIOINforMA BIOINformatics FOR MArine biology Bioinformatics Services, Consultancy and Education )

OnLine resource: Superficial sea temperature trend map at low spatial resolution (~5km) ( WWW:LINK-1.0-http--link )
pass: nX8wa45nYB

Hierarchy level: Model

Other: raster

Conformance result

Date ( Revision ): 2020-08-04

Explanation: Validated in Geonetwork according to the ISO19115 rules (23/23) and the INSPIRE rules (29/29)

Pass: Yes

Statement: The rasters were built in R statistical software using public available data downloaded from Copernicus Marine Environment Monitoring Service (https://resources.marine.copernicus.eu/?option=com_csw&task=results). The netCDF files were converted into 3-dimensional arrays (longitude, latitude and time) and reanalyzed in order to extract the trend value of each pixel over the time period and over a specific quarter of the time period. The trend was calculated using a linear regression with data aggregated by year.