IV VALIDATION RESULTS
IV.1.2 Validation synthesis
Although we cannot provide a comparison of in situ measurements with a reference (in situ measurements are the general references), we can provide a global idea of the dataset quality through the residual value (equivalent of altimetry measurements).
Considering drifter’s velocity and temperature measurements, the validation relies on provider (AOML quality control).
About the wind slippage correction, a statistical comparison to geostrophic current derived from altimetry is performed. The results from drogued and corrected undrogued drifters are evaluated. The resulting statistical comparisons with satellite geostrophic current are close when using drogued drifters or undrogued drifters corrected from windage. This correction is then well defined and evidences the error done on the velocity estimation from undrogued drifters.
We also have identified some error sources for the remaining RMS differences of Table 9 and Table 10 (near 0.10 m.s-1):
- ARGOS location errors and kriging method;
- Empirical Ekman/Stokes model used to remove the ageostrophic component;
- The partly unfiltered (by the low pass filter) or unresolved ageostrophic components (part of the Stokes drift, inertial oscillations, internal waves);
- Altimetric measurement and geostrophic current error (spatial and temporal resolution).
- ERA5 wind stress which has shown to introduce some bias in the surface Ekman model in the tropics and western boundary currents (CMEMS-MOB-QUID-015-004)
IV.2 Dataset: radar_total
Different steps have been followed to assess the radar_total data uncertainty. As described by Lipa (2013), if we assume that the radar hardware is operating correctly, we can identify different sources of uncertainty in the radial velocities: (a) variations of the radial current component within the radar scattering patch; (b) variations of the current velocity field over the duration of the radar measurement;
(c) errors/simplifications in the analysis (e.g. incorrect antenna patterns or errors in empirical first order line determination); (d) statistical noise in the radar spectral data, which can originate from power-line disturbances, radio frequency interferences, ionosphere clutter, ship echoes, or other environmental noise (Kohut and Glenn, 2003). When dealing with total currents, additional geometric errors can affect the accuracy of the HFR data. These errors (GDOP and GDOSA) are distributed spatially and can be controlled and estimated in the processing from total to radials (Chapman et al., 1997; Barrick, 2002).
The EU HFR Node is in charge of checking the validity of the HF radar data files and of applying the NRT QC in compliance with the EU Standard for HF radar surface current data. The mandatory metrics to be applied according to the European common QC standard for NRT HFR current data are summarized in section III.2. For the release of NRT surface total currents, the evaluation of the radar_total data set
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1
These validation exercises can be limited by the fact that part of the discrepancies observed through these comparisons are due to the specificities and own inaccuracies of the different measuring systems.
Several examples are provided in the NRT QUID for three different HFR systems: the Gulf of Manfredonia, the Ibiza Chanel (HFR-Ibiza) and the SE Bay of Biscay (HFR-EUSKOOS; see details in http://marine.copernicus.eu/documents/QUID/CMEMS-INS-QUID-013-048.pdf
In conclusion, the validation results for the three analysed areas showed a good agreement between HFR and ADCP, drifters and current meter data. With correlation ranging between 0.6 and 0.8 for most of the cases where subsurface current data from moorings or drifters within (or close to) the vertical range of the HFR measurements are used, with similar results to that found in the literature.
For the QA/QC of the REP data series, a comprehensive examination of the data series available and the performance of the systems has been performed. The results for each of the system have been summarized in a dedicated short report, containing the figures described in the previous sections and the main conclusions derived from them. In the next tables, the main the analysis of the systems performance based on the 80/80 % metrics (Table 11 and Erreur ! Source du renvoi introuvable.) and the link (Table 12) to the reports are summarized.
Year /System 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 20201
HFR-Gibraltar 46.192 59.41
HFR-Ibiza 75.77 59.18 70.11 76.52 79.8 89.14 65.15 76.01 55.79
HFR-DeltaEbro 82.192 76.25
HFR-TirLig 78.333 89.81 35.64 26.73 35.16
HFR-Vigo 91.72 91.69 90.52 93.02 97.44 98.13 97.445
HFR-US-Alaska 4.42 0
HFR-US-EastGulfCoast 22.02 21.31
HFR-US-Hawaii 32.02 43.40
HFR-US-PuertoRicoVirginIslands 20.04 7.55
HFR-US-WestCoast 55.56 4.7
Table 11: Summary of QA/QC analysis of REP data. Results of the 80/80 metrics. Percent of spatial coverage available for each of the systems in the displayed periods. In green systems achieving or approaching the 80/80 goal, in orange systems with spatial coverage between 40 and 70%, in red systems with spatial coverage under 40%; (1) Only period Jan-Jul 2020; (2) Only period Oct-Dec 2019; (3) Only period Aug-Dec 2016; (4) Only period Jun-Dec 2015; (5) Only period Jan-May 2016. In the case of the US systems (in light blue), the metrics are computed for very large geographical areas. Higher 80/80 scores can be observed for specific subregions (please refer to Figure 28 and the reports in Table 11 for more detailed information).
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1
Figure 28: Map of the % of availability of data in each grid point and contour showing the area of temporal availability >80%, for the indicated HFR systems. The maps are computed following Roarty et al. (2012), for each system independently and for the corresponding periods as specified in the title of the subplots (continues in next page).
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1
Figure 28: (continued) Map of the % of availability of data in each grid point and contour showing the area of temporal availability >80%, for the indicated HFR systems. The maps are computed following Roarty et al. (2012), for each system independently and for the corresponding periods as specified in the title of the subplots.
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1
HFR System Link to system reports and figures
HFR-COSYNA http://dspace.azti.es/handle/24689/888
Table 12: Summary of QA/QC analysis of REP data. Link to system reports.
IV.3 Dataset: adcp
For research vessel ADCP observation, each cruise is processed by a data processing chain such as Cascade, quality controlled and visually inspected.
The cruises data are then aggregated into in situ TAC NetCDF files.
The final visual inspection and assessment of the in situ TAC is performed on a series of graphics
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1
For each vessel file, the parameters are plotted and inspected:
● eastward_sea_water_velocity, northward_sea_water_velocity, upward_sea_water_velocity, depth
Figure 29: The depth bins of Beautemps-Baupré RV ADCP, from 0 to 100 meter deep
Figure 30: The eastward_sea_water_velocity from Beautemps-Baupré RV ADCP
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1
Figure 31: The northward_sea_water_velocity from Beautemps-Baupré RV ADCP
QUID for Global Ocean-Delayed Mode in-situ Observations of surface (drifters and HFR) and
sub-surface (vessel-mounted ADCPs) water velocity INS_GLO_UV_L2_REP_OBSERVATIONS_013_044
Ref : CMEMS-INS-QUID-013-044 Date : 28 October 2020
Version : 2.1