MeLa: A Programming Language for a New Multidisciplinary Oceanographic Float

At 2000 m depth in the oceans, one can hear biological, seismological, meteorological, and anthropogenic activity. Acoustic monitoring of the oceans at a global scale and over long periods of time could bring important information for various sciences. The Argo project monitors the physical properties of the oceans with autonomous floats, some of which are also equipped with a hydrophone. These have a limited transmission bandwidth requiring acoustic data to be processed on board. However, developing signal processing algorithms for these instruments requires one to be an expert in embedded software. To reduce the need of such expertise, we have developed a programming language, called MeLa. The language hides several aspects of embedded software with specialized programming concepts. It uses models to compute energy consumption, processor usage, and data transmission costs early during the development of applications; this helps to choose a strategy of data processing that has a minimum impact on performances. Simulations on a computer allow for verifying the performance of the algorithms before their deployment on the instrument. We have implemented a seismic P wave detection and a blue whales D call detection algorithm with the MeLa language to show its capabilities. These are the first efforts toward multidisciplinary monitoring of the oceans, which can extend beyond acoustic applications.

Spatio-temporal dependence-based tensor fusion for thermocline analysis in Argo data

As the ocean data acquired by the Argo project is increasingly huge, how to use artificial intelligence to analyze it so as to discover the distribution and variation of ocean temperature with space and time becomes an important research topic in the world. In this article, a spatio-temporal dependence-based tensor fusion method is proposed, which can be used to determine and analyze the thermocline. In the time dimension, long short-term memory is used to predict the temperature of seawater; in the spatial dimension, the thermocline is found incrementally by using tensor analysis. Experiments on BOA Argo data from 2004 to 2016 show that the proposed method can accurately determine the boundary of the thermocline and predict the future trend of the thermocline.

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

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.