Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time
Abstract. Currently there is a huge amount of freely available hydrographic data and it is increasingly important to have access to it efficiently and easily provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data goes through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd-even algorithm, this allows analysis of a study area, regardless of size, shape or location. Also, gives two filtering methods to discard only the real time quality control data that present salinity drifts, thus taking advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter and 30 % in the case of the second of the total real time quality control data that are usually discarded due to problems such as salinity drifts, this allows researchers to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, unlike waiting for the delayed mode quality control that takes up to 12 months to be completed.