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A Real Time Synoptic View Of Ocean Currents

By Dr. Marc Lucas • Dr. Héléne Etienne • Dr. Eric Greiner

A schematic diagram of the HCS.
Getting an accurate picture of oceanic surface conditions is crucial for most seagoing activities. Traditional observing methods often only provide single-point data (i.e., a rotor current meter) or, at best, one-dimensional profiles (i.e., an acoustic Doppler current profiler, or ADCP) and require considerable effort to install and maintain, making it difficult to get a synoptic view of the ocean. This is a significant drawback as a synoptic vision is fundamental if the user is to get a feel and an understanding of the oceanic conditions in his area of interest, in a similar way to what has been achieved in meteorology.

Satellite data have gone a long way in providing this synoptic vision, with the introduction of very high-resolution infrared sea surface temperature and ocean color data. However, satellites do not provide direct measurement of ocean currents, which is the variable that is of greatest interest. In recent years, numerical models have been developed to answer this need, but, in spite of advances in data assimilation techniques and resolution, the scientific complexity and numerical cost often mean that they fall short of the end user's expectations.

Therefore, there is a need to develop a method where satellite data can provide an operational delivery of current data in a given region. To this end, CLS (Ramonville-St.-Agne, France) created the Hybrid Current System (HCS), which incorporates relevant and available satellite data in a way that is made to be easily deployable, relocatable and computationally inexpensive. This system is designed to answer the need for ocean surface currents in hindcast as well as in forecast situations.

Hybrid Current System Design
The HCS combines fluid dynamic equations with near-real-time satellite observations and 3D ocean general circulation model (OGCM) analysis to provide the user with a three-day forecast. Data are introduced into the model using spectral nudging techniques and damping coefficients. Depending on the available data, each model's variables (e.g., velocity, meridional velocity, model layer depth) are damped on different spatial scales.The high-resolution velocity of the model comes from satellite measurements, and the mesoscale is derived from a combination of OGCM and satellite altimetry data. A global tide model is used to add the tidal component. To calibrate the model parameters, in-situ data are drawn from drifting floats and ADCPs, which, in this case, included CLS's MAR-GE/T drifters and the Teledyne RD Instruments (Poway, California) 300-kilohertz Workhorse Monitor ADCPs. Data from tidal gauges can also be used to improve the tidal model.

The HCS is fully dependant on the quality of the input data used to constrain and force the model. Drifters or any current measurement data can be used in order to correct the produced current forecast in an a-posterior treatment. The final field is often more realistic and closer to the observations, with a more accurate positioning of dominant features, such as eddies.

Data Sources
An important feature of the HCS is the access to multiple sources of data. Depending on the specific area chosen, different products will be used to represent specific scales of the dynamics.

Satellite data offer numerous advantages over traditional ocean observing techniques, such as offering 2D spatial coverage, being nearly global and remaining unaffected by issues such as the remoteness of the location or the local weather conditions (although cloud cover can be an issue). Satellite data time coverage is also better, as the data are often continuous, with time series going back as far as 20 years. In addition, the resolution of satellite data is very high and able to reach down to a few meters for synthetic aperture radar imagery, although it is often in the range of a few kilometers for most products. Furthermore, the near-real-time delivery of the data takes a few hours, which is quicker than the characteristic time scales of many ocean phenomena.

Two aspects of the data determine its suitability for use by the HCS: quality and availability in real and near-real time, and in forecast modes. Hence, if consistent with the HCS's needs, priority is given to these data sets in operational mode. There are three types of data used in the production of the HCS currents.

Satellite Data. Satellite data are used to provide information on the large-scale circulation (altimetry data, often from CLS's Aviso Web server) through the geostrophy calculation. It is also used to give information on the small-scale features, such as sea surface temperature and ocean color data, through the surface quasigeostrophic approach. Sea surface temperature and ocean color data are operationally produced at CLS.

Model Data. Three types of model data are used in the HCS: Ocean, atmospheric and tidal. Ocean model data provide boundary conditions and information on the large-scale velocity intensity distribution, often from the Mercator Ocean (Ramonville-St.-Agne) model but also from regional models when available. These operational models use in-situ observation and along-track satellite data, such as altimetry.

Atmospheric data are used to calculate the wind-driven part of the flow, often from the European Centre for Medium-Range Weather Forecasts (ECMWF) model and from available local configurations. To continue this article please click here.

Dr. Marc Lucas oversees CLS's operational modeling as a project and research engineer. He develops, sets up and runs numerical models and data-processing tools. He also regularly conducts metocean studies, and research and development involving satellite data. He completed his Ph.D. in numerical modeling in 2005.

Dr. Héléne Etienne completed her Ph.D. in data assimilation in 2003. Since then, she has worked in collaboration with Mercator Ocean in the field of operational oceanography. In the last few years, she has developed and implemented numerical modeling in surface and 3D configurations at CLS, where she works as a research engineer.

Dr. Eric Greiner, a research engineer at CLS, completed his Ph.D. in numerical analysis in 1993 and has worked in the field of numerical modeling since. For the past 10 years, he has been in charge of scientific innovation in operational modeling and validation. He collaborates with research institutions around Europe.

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