A Statistical Method for Correcting Salinity Observations from Autonomous Profiling Floats: An ARGO Perspective

Abstract

The Profiling Autonomous Lagrangian Circulation Explorer (PALACE) float is used to implement the Array for Real-Time Geostrophic Oceanography (ARGO). This study presents a statistical approach to correct salinity measurement errors of an ARGO-type fleet of PALACE floats. The focus is on slowly evolving drifts (typically with time scales longer than a few weeks). Considered for this case study is an ensemble of about 80 floats in the Irminger and Labrador Seas, during the 1996?97 period. Two different algorithms were implemented and validated based on float-to-float data comparison at depth, where the water masses are relatively stable over the time scales of interest. The first algorithm is based upon objective analysis of the float data, while the second consists of a least squares adjustment of the data of the various floats. The authors? method exhibits good skills to retrieve the proper hydrological structure of the case study area. It significantly improves the consistency of the PALACE dataset with in situ data as well as with satellite altimetric data. As such, the method is readily usable on a near-real-time basis, as required by the ARGO project.