Combining Argo and Satellite Data Using Model-Derived Covariances: Blue Maps

Abstract

Blue Maps aims to exploit the versatility of an ensemble data assimilation system to deliver gridded estimates of ocean temperature, salinity, and sea-level with the accuracy of an observation-based product. Weekly maps of ocean properties are produced on a 1/10°, near-global grid by combining Argo profiles and satellite observations using ensemble optimal interpolation (EnOI). EnOI is traditionally applied to ocean models for ocean forecasting or reanalysis, and usually uses an ensemble comprised of anomalies for only one spatiotemporal scale (e.g., mesoscale). Here, we implement EnOI using an ensemble that includes anomalies for multiple space- and time-scales: mesoscale, intraseasonal, seasonal, and interannual. The system produces high-quality analyses that produce mis-fits to observations that compare well to other observation-based products and ocean reanalyses. The accuracy of Blue Maps analyses is assessed by comparing background fields and analyses to observations, before and after each analysis is calculated. Blue Maps produces analyses of sea-level with accuracy of about 4 cm; and analyses of upper-ocean (deep) temperature and salinity with accuracy of about 0.45 (0.15) degrees and 0.1 (0.015) practical salinity units, respectively. We show that the system benefits from a diversity of ensemble members with multiple scales, with different types of ensemble members weighted accordingly in different dynamical regions.