Argo QC data

Introduction

According to wikipedia, `Argo is an observation system for the Earth's oceans that provides real-time data for use in climate, weather, oceanographic and fisheries research.Argo consists of a large collection of small, drifting oceanic robotic probes deployed worldwide. The probes float as deep as 2 km. Once every 10 days, the probes surface, measuring conductivity and temperature profiles to the surface. From these, salinity and density can be calculated.'

There a number of Data Assembly Centre (DACs) which use the Argo data to model flow in the ocean. They have to make almost real time decisions on whether the argo data they receive is good or bad, depending on whether it passed their quality control (QC).

About six months later, more sophisicated QC test are applied to the Argo data to determine if the data was good or bad, known as the delayed mode QC data. This the nearest thing we have to the truth, and is treated as so here. This project looks to compare when the delayed mode QC data considers measure to be good or bad with whether the DACs accepted or rejected the data in their almost real time decisions.

The hope is that this comparisons can aid the DACs in improving their skill and determining whether to accept or reject Argo data.