Abstract

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns.

Highlights

  • The expanding operational capability of global monitoring from space, combined with data from long-term Earth Observation (EO) archives, in situ networks, and models, will provide users with unprecedented insights into environmental data from space

  • Salinity fields at the surface and 1◦ resolution grid are used as a complement to the in situ analysis system (ISAS) to characterize the climatological components at the match-up pairs’ locations and dates

  • match-up database (MDB) data consist of satellite and in situ sea surface salinity (SSS) pairs and of auxiliary geophysical parameters such as the local history of wind speed and rain rates, as well as various pieces of information that can be derived from in situ, model, or satellite data and which are included in the final match-up files

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Summary

A Hub for Validation and Exploitation of Satellite Sea Surface

Sébastien Guimbard 1, * , Nicolas Reul 2 , Roberto Sabia 3 , Sylvain Herlédan 4 , Ziad El Khoury Hanna 4 , Jean-Francois Piollé 2 , Frédéric Paul 2 , Tong Lee 5 , Julian J. Bingham 7 , David Le Vine 8 , Nadya Vinogradova-Shiffer 9 , Susanne Mecklenburg , Klaus Scipal and Henri Laur 11. Z.E.; Piollé, J.-F.; Paul, F.; Lee, T.; Schanze, J.J.; Bingham, F.M.; et al. Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations

Introduction
In Situ SSS Datasets
13 November 2017
Thermosalinograph Dataset
Surface Drifters
Marine Mammals
Moorings
SPURS-2 Field Campaign Datasets
In Situ SSS Analyses and Climatologies
SSS Satellite Products
Thematic Datasets
Precipitation
Surface Wind Speed
Sea Surface Temperature
Models and Assimilation
Other Auxiliary Datasets
Overview of the Match-Up Generation Method
MDB Pair Co-Localization with Auxiliary Data and Complementary Information
Distribution of In Situ SSS Depth Measurements
Spatial Distribution of Match-Ups
Match-Up Analysis Report
Scatterplots of Satellite versus In Situ SSS by Latitudinal Bands
Exploitation
Syntool
Plot Interface
Match-Up Interface
Merginator
Jupyter Notebooks
Case Studies
Case Study 1
Case Study 2
Case Study 3
Case Study 4
Findings
Summary and Conclusions
Full Text
Published version (Free)

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