The impact of the number of satellite altimeters providing sea surface height anomaly (SSHA) information for a data assimilation system is evaluated using two comparison frameworks and two statistical methodologies. The Naval Research Laboratory (NRL) Layered Ocean Model (NLOM) dynamically interpolates satellite SSHA track data measured from space to produce high-resolution (eddy resolving) fields. The Modular Ocean Data Assimilation System (MODAS) uses the NLOM SSHA to produce synthetic three-dimensional fields of temperature and salinity over the global ocean. A series of case studies is defined where NLOM assimilates different combinations of data streams from zero to three altimeters. The resulting NLOM SSHA fields and the MODAS synthetic profiles are evaluated relative to independently observed ocean temperature and salinity profiles for the years 2001–03. The NLOM SSHA values are compared with the difference of the observed dynamic height from the climatological dynamic height. The synthetics are compared with observations using a measure of thermocline depth. Comparisons are done point for point and for 1° radius regions that are linearly fit over 2-month periods. To evaluate the impact of data outliers, statistical evaluations are done with traditional Gaussian statistics and also with robust nonparametric statistics. Significant error reduction is obtained, particularly in high SSHA variability regions, by including at least one altimeter. Given the limitation of these methods, the overall differences between one and three altimeters are significant only in bias. Data outliers increase Gaussian statistical error and error uncertainty compared to the same computations using nonparametric statistical methods.

Global satellite missions with the capability to measure ocean surface currents are continually being proposed. This new observation type is expected to significantly improve ocean model analysis and forecast skill. The potential impact of assimilating sea surface currents from the proposed wide-swath Ocean Dynamics and Surface Exchange with the Atmosphere (ODYSEA) mission is investigated in this study. An Observing System Simulation Experiment (OSSE) is set up with a 1 km Navy Coastal Ocean Model (NCOM) analysis-forecasting system in the Gulf of America domain over a 4-month time period. When compared to an experiment with only the standard data streams of temperature, salinity, and sea surface height anomaly observations from in situ and satellite platforms assimilated, the inclusion of ODYSEA-like sea surface current observations leads to a 13% and 17% reduction in the domain and time averaged root mean squared error (RMSE) for surface u and v components, respectively, as well as an improvement in the current velocity throughout the upper water column. The assimilation of the sea surface current observations also leads to an improvement in the model sea surface height, although there is a negligible to slight degradation in the temperature and salinity at depth, which is likely due to the explicit geostrophic assumption made within the velocity assimilation methodology.

Upper ocean variability depends on tides and wind forcing. The barotropic tide creates baroclinic internal tides and internal gravity waves (IGWs) that can propagate long distances and dissipate, impacting mixed layer and thermocline depths, vertical thermal gradients, and mixing. In the last decade, there has been an effort to include tidal motions in global HYCOM (HYbrid Coordinate Ocean Model). Here, we look at the impacts of tidally driven motions on acoustic propagation along the Amazon shelf by comparing acoustic transmission loss and key acoustic parameters in 1/25 deg Global HYCOM model runs with and without tides. Acoustic runs were performed at mid-frequencies using Bellhop3D at depths above and below the sonic-layer depth (SLD). Key acoustic parameters assessed include the SLD and vertical sound-speed gradients. Although the impacts of the internal waves and tides on acoustics are intermingled with the effects of other stochastic ocean processes (e.g., mesoscale eddies, fronts, and atmospheric forcing), timeseries analysis indicates that there is acoustic sensitivity to tidally forced variability. These ocean-acoustic results can be used for future development of machine learning algorithms to model coupled oceanographic and acoustical processes.