Ocean Data Impacts in Global HYCOM

Abstract

he impact of the assimilation of ocean observations on reducing global Hybrid Coordinate Ocean Model (HYCOM) 48-h forecast errors is presented. The assessment uses an adjoint-based data impact procedure that characterizes the forecast impact of every observation assimilated, and it allows the observation impacts to be partitioned by data type, geographic region, and vertical level. The impact cost function is the difference between HYCOM 48- and 72-h forecast errors computed for temperature and salinity at all model levels and grid points. It is shown that routine assimilation of large numbers of observations consistently reduces global HYCOM 48-h forecast errors for both temperature and salinity. The largest error reduction is due to the assimilation of temperature and salinity profiles from the tropical fixed mooring arrays, followed by Argo, expendable bathythermograph (XBT), and animal sensor data. On a per-observation basis, the most important global observing system is Argo. The beneficial impact of assimilating Argo temperature and salinity profiles extends to all depths sampled, with salinity impacts maximum at the surface and temperature impacts showing a subsurface maximum in the 100?200-m-depth range. The reduced impact of near-surface Argo temperature profile levels is due to the vertical covariances in the assimilation that extend the influence of the large number of sea surface temperature (SST) observations to the base of the mixed layer. Application of the adjoint-based data impact system to identify a data quality problem in a geostationary satellite SST observing system is also provided.