## 5 Conclusions  
We have generated simulated pseudo-SWOT data for the western Mediterranean Sea using a SWOT simulator and outputs from an ocean numerical model. To evaluate the output SWOT data, we derived absolute geostrophic velocities and relative vorticities from the pseudo-SWOT SSH data. We find that, due to the satellite's instrumental noise and geophysical errors, the features observed in the pseudo-SWOT SSH are lost in the derived dynamical variables. Looking at the spatial spectra, we find that noise dominates the signal at wavelengths smaller than 60 km. We applied a Laplacian diffusion filtering technique to attempt to remove the noise and hence observe finer scales. We estimated the appropriate cut-off wavelength for each parametrization. To filter out the noise, we applied a series of ascending cut-off wavelengths: 15, 30, 40, 50, 60, 100 and 200 km. We find that in this study region, using this technique, we cannot resolve the expected 15 km wavelength. On the other hand, we are able to recover the signal within a swath down to a 40-60 km wavelength range. This is still an improvement in comparison to wavelengths resolved by present-day 1D altimeters, especially at eastern basins. Robust swath-filtering is an important first step towards meeting our goals for reconstruction techniques that will enable us to combine SWOT and altimetric data in order to produce gridded SSH maps of significantly higher resolution than contemporary products. New versions of the SWOT simulator code include improved representation of instrumental and geophysical errors, and also give us the option to obtain pseudo-data for the SWOT fast-sampling phase. New pseudo-SWOT data will allow us to better refine the results of this study and to examine a wider range of scenarios.  
**Acknowledgments:** The research leading these results has received funding from the Sea Level Thematic Assembly Center (SL-TAC) of the Copernicus Marine and Environment Monitoring Service (CMEMS) and from the Centre National d'Etudes Spatiales (CNES) through Ocean Surface Topography Science Team (OST/ST) project MANATEE. [PERSON] acknowledges CNES and FP-7 PhD funding. This work was supported by a Short Term Scientific Mission (STSM) grant from COST Action ES140. [PERSON] was supported by the Copernicus Marine Environment Monitoring Service (CMEMS) MedSUB project. [PERSON] and [PERSON] are supported by the CNES through the OST/ST and the SWOT Science Team. The WMOP simulation used in this study was produced in the framework of the MEDCLIC project funded by \"La Caixa\" Foundation. This study is a contribution to the PRE-SWOT project (CTM2016-78607-P) funded by the Spanish Research Agency and the European Regional Development Fund (AEI/FEDER, UE). Codes and output files are available online at the project repository ([[https://github.com/[PERSON]/paper_[PERSON]_etal_2018](https://github.com/[PERSON]/paper_[PERSON]_etal_2018)]([https://github.com/[PERSON]/paper_[PERSON]_etal_2018](https://github.com/[PERSON]/paper_[PERSON]_etal_2018))).  
**Author Contributions:** [PERSON] and [PERSON] designed the study; [PERSON], [PERSON] and [PERSON] designed the filtering experiments; [PERSON] provided the WMOP Simulation data and helped on the analysis of the model data; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the analysis of the results; [PERSON] wrote the manuscript and all authors contributed to the writing.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.