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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

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Simulated Sea Surface Salinity Data from a 1/48° Ocean Model.

Frederick M Bingham¹, Séverine Fournier², Susannah Brodnitz³

¹Center for Marine Science, University of North Carolina Wilmington, Wilmington, NC, 28403, USA. binghamf@uncw.edu.

Scientific Data

|May 23, 2024

Summary

This summary is machine-generated.

This study simulates satellite and in situ sea surface salinity (SSS) data using a high-resolution ocean model. The simulated data aids in understanding satellite SSS validation, including sampling errors and subfootprint variability.

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Area of Science:

Oceanography
Remote Sensing
Climate Modeling

Background:

Accurate sea surface salinity (SSS) measurements are crucial for understanding ocean circulation and climate.
Satellite missions like Aquarius, SMAP, and SMOS provide global SSS data, but validation requires high-quality reference data.
The ECCO global ocean model offers a high-resolution dataset for simulating satellite and in situ SSS.

Purpose of the Study:

To generate simulated satellite and in situ SSS data for validating satellite SSS measurements.
To investigate sampling errors, matchups, and subfootprint variability in SSS data.
To assess the validation process for SSS data at Level 2 and Level 3.

Main Methods:

Utilized the ECCO (Estimating the Circulation and Climate of the Oceans) 1/48° global ocean model simulation.
Extracted satellite ground tracks (Aquarius, SMAP, SMOS) to sample model data, simulating Level 2 (L2) SSS.
Averaged L2 data onto a regular grid to produce simulated Level 3 (L3) SSS and generated simulated Argo and tropical mooring datasets.

Main Results:

Generated one year of simulated SSS data (November 2011-October 2012) at L2 and L3 resolutions.
Created simulated Argo and tropical mooring SSS datasets, including a gridded monthly 1° Argo product.
The simulated data enabled the study of sampling errors, matchups, and subfootprint variability.

Conclusions:

The simulated SSS datasets provide a valuable tool for studying satellite SSS validation processes.
Understanding subfootprint variability and sampling errors is essential for accurate SSS data validation.
This approach enhances the reliability of satellite-derived SSS for climate and oceanographic research.