Scientific document

The surface drifter data contained in this database come from various buoy designs, including the modified CODE, CODE/Tz, SVP/OCM drifters. Most systems (e.g., the modified CODE type) measure the surface currents within the first top meter of water column. Others were drogued at 15 m depth.  All drifters measure Sea Surface Temperature (SST). Some drifters measure temperature at various levels in the water column with a thermistor chain (CODE/Tz). Others measure water optical properties (SVP/OCM).

The drifters were released in the Middle Adriatic between March 2006 and August 2006 during oceanographic surveys conducted onboard NRV Alliance (DART06a and DART06b sea trials).

All drifters were tracked by, and transmitted data to, the
Argos Data Collection and Location System (DCLS) onboard the NOAA polar-orbiting satellites. Transmissions were programmed every 90 seconds. In addition, some drifters were fitted with GPS receivers to obtain more accurate position at 1 h intervals.

2. Data Reduction and editing

2.1 Drifter Data reduction

The data for each drifter were read, reduced and written into individual files. The sensor data were processed and reduced in the following way. The sensor data (i.e., time, voltage, temperature and GPS position) records telemetered during a single satellite overpass were decompressed, that is, each record was repeated by a number of times equal to a given compression index and the repeated records were shifted back in time by successive 90 s increments. For the drifters without GPS receiver, the data were then sorted in increasing sequential order and the median values were estimated. These median statistics were assigned to the drifter Argos location and were written into the output raw file. For the passes with good sensor data but for which no drifter location was provided by Service Argos, the output raw latitude and longitude were assigned with the NaN default value. For the drifters equipped with a GPS receiver, the position and sensor times are identical and the sampling interval is constant (1 h). No median operator was applied to the data.

During the reading and reduction process, the times were converted into MATLAB time (serial days where 1 corresponds to 01-Jan-0000). The deployment coordinates (time, latitude, longitude and bucket SST) were added to the drifter time series as the initial record. A location class 4 was assigned to this record. 

2.2 Determination of time of last good fix and type of death

The type of dysfunction or the circumstances of the termination receipt of good quality oceanographic data have been carefully investigated by examining the suspect records in the context of their proximity to the coast line, the values and the probability that they were picked up by seafarers. Thus, the time for the last good fix was determined and the type of death was classified into one of five categories: Recovered (intentionally), Grounded; Picked-up; Battery failure; Unknown.  For drifter equipped with GPS receivers, the start and end times of the Argos and GPS position time series were also identified. 

2.3 Data editing

The position and temperature data were edited through automatic and manual procedures. The criteria utilized in the automatic procedure for the position data are based on a maximum distance of 1000 m, a maximum speed of 50 cm/s and a maximum angle of 45 degrees, between successive points. This means that the longitude and latitude of a point are flagged with NaN if  (i) the distances with the previous and successive points are greater than the limit;  (ii) the previous or the successive velocities are greater than the limit and (iii) the angles formed with the previous and successive points are both within 180+/-45 degrees . This procedure is iterated twice. Thereafter, a manual procedure is performed to eliminate the remaining residual outliers visualized on the drifter tracks.

The temperature data were edited using the following conditions: the temperatures are flagged with NaN if (i) the temperature gradient between successive points exceeds 1/8 degrees/hour and (ii) the temperature difference with respect  to a blackman running mean exceeds 5, 4, 3, 2 and 1 degree (in iterative successive runs). The editing based on the maximum gradient is then repeated. Finally the manual procedure is performed to  remove remaining outliers appearing in time series plots.

3. Data interpolation and filtering

The despiked data were interpolated onto regular 2-hour intervals using an optimum analysis technique known as Kriging.  The Kriging used here employed an analytic function fit to the empirical structure function computed from the entire despiked data set. Both the interpolated value and an estimate of its accuracy were computed.

The interpolated positions and temperature were then low-pass filtered with a Hamming filter with cut-off period at 36 hours  in order to remove high frequency current components, especially the strong tidal and inertial currents. The low-pass time series were finally subsampled every 6 hours and the velocity was computed by finite centered differencing the 6-hourly interpolated/filtered position data. The processed data files contain 6-hourly values of position, velocity and temperature. The velocity for the first and last records of each drifter, the temperature after failure of the SST sensor, and all the variables during temporary grounding, were assigned the NaN default value. If the time difference between the interpolated point and the closest edited observation is larger than 3 days, the corresponding velocity was assigned NaN in order to avoid meaningless interpolated velocity estimates in large data gaps.