The L4 products are gridded products that merge the information from the different altimeters available. They can be used to compute derived products such as the geostrophic sea surface currents. The L4 product generation processing methodology consists in an optimal interpolation processing as fully synthesized in Pujol et al (2016).
The L4 gridded products are generated using an optimal interpolation method as described by Bretherton et al. (1976). This methodology allows us to obtain a formal error associated to the optimal interpolation output.
In the REP/DT processing, the products can be computed optimally with a centered computation time window of ±6weeks around the date of the map to be computed.
In the NRT processing, contrary to REP/DT case, the products cannot be computed with a centered computation time window: indeed, as the future data are not available yet, the computation time window cannot be centered. Only data over the period [D-7weeks, D] are used, where D is the date of the production considered. For each day of NRT production, three merged maps are produced daily and delivered to the users (figure):
- A 0-day delay (i.e. map for day D), which represents a preliminary map production
- A 3-day delay (i.e. map for day D-3days), which represents an intermediate map production. When available, this map replaces the 0-day delay map
- A 6-day delay (i.e. map for day D-6days), which represents a final NRT map production. When available, this map replaces the 3-day delay map
Both for the REP and NRT, the maps are dated on mid-night.
Note however that the spatial and temporal scales of the variability that is resolved in the DUACS merged products data set are imposed by the temporal correlation function used in the OI mapping procedure, as described in Pujol et al (2016)and Taburet et al (2019).
Number of satellites to compute the maps
Both in REP/DT and NRT processing, the maps are computed with all the satellites available. This allows an improved signal sampling when more than 2 altimeters (corresponding to the minimal constellation) are available. The mesoscale signal is indeed more accurately reconstructed during these periods (Pascual et al, 2006), when omission errors are reduced by the altimeter sampling. The all-sat-merged series is however not homogeneous in time due to the evolutions of the altimeter constellation (see QUID document)
Formal mapping error
The formal mapping error does not represent the precision of the SLA gridded products but it represents an excellent indicator of the consistency of the grid. In practice, this formal error variance corresponds to a local minimum in the least squares senses. It depends on the constellation sampling capability (i.e. spatial distribution and the density of the data used in the suboptimal estimation) and its consistency with the spatial/temporal scales and sea surface variability considered, and also on the noise budget for the different measurements used, as described in Le Traon et al (1998) or Pujol et al (2016).
The formal mapping error is usually low under the tracks of the different altimeters used in the mapping. It is higher within the inter-track diamonds. Higher formal mapping error is also observed over high variability areas.
The Quality Control is the final process used by DUACS before product delivery. In addition to daily automated controls and warnings to the operators, each production delivers a large QC Report composed of detailed logs, figures and statistics of each processing step. Altimetry experts analyze these reports twice a week (only for internal validation, those reports are not disseminated).