dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of DUACS DT2021 products in the retrieval of sea level in the coastal band This section presents the statistics of the comparisons performed between the DUACS DT2021 all satellites and two satellites datasets; and the tide gauge observations from the Copernicus catalogue in the coastal region of the European Seas in terms of errors (rms differences) and variance of the differences between the datasets. According to  Sánchez-Román et al. (2020) , the bootstrapping technique was applied to gain an estimation of the standard errors of the differences between the datasets. The mean value of the rms difference between the all satellites dataset and tide gauges is 4. 11 cm; the variance of the differences (altimetry-tide gauge) is 17 cm 2 ; and the mean distance between the location of the tide gauge and the corresponding altimeter data with the highest correlation is 82 km (Table  1 ).
These values raise to 4. 35 cm, 19 cm 2 , and 87 km, respectively, when using the two satellites dataset. The tide gauge stations (213 stations) common to both datasets were used. Thus, the all satellites dataset reduces the rms differences with tide gauges in the European coasts by 5%; the variance differences between the datasets by 10%. The mean distance between the most correlated altimetry point and tide gauges is reduced by 6%. Also, the number of valid data pairs used to conduct the intercomparison enhanced by 0. 2% when using the all satellites dataset. This is due to the larger number of satellite missions used to generate this dataset, that provides lower errors in the optimal interpolation procedure compared to the two satellites dataset. from the bootstrap method using 1. 000 iterations.
DUACS DT2021 all Fig. 1  shows the consistency between the DUACS DT2021 all satellites dataset and the tide gauge data computed from Eq. (1) in  Sánchez-Román et al. (2020). Consistency is expressed as the mean square differences between both datasets, computed as the variance of the differences (altimetry-tide gauge), in terms of percentage of the tide gauge variance. Overall, mean square differences lower than 5 % are observed in the central and eastern parts of the Baltic Sea, this emphasising the precision of the corrections applied to the altimeter data in the basin; whereas they reach values between 20% and 30% for stations located in the connection region with the North Atlantic Ocean. The mean square differences are between 20% and 50% for most of the stations located along the Atlantic shore; this including the Strait of Gibraltar area.
all satellites SLA dataset This section focuses on the statistics of the comparisons performed between the DUACS DT2021 and DT2018 reprocessing all satellites datasets; and the tide gauge observations. The mean value of the rms difference between the DT2018 processing dataset and tide gauges is 4. 22 cm; the variance of the differences (altimetry-tide gauge) is 18 cm 2 ; and the mean distance between the location of the tide gauge and the corresponding altimeter data with the highest correlation is 88 km (Table  2 ). Overall, these values are larger than those reported in the previous section for the comparison using the DT2021 processing dataset (see Table  1 ). As a consequence, the DT2021 all satellites dataset reduces (i) the errors with tide gauges in the European coasts by 3%, (ii) the variance of the differences between the datasets by 5%, and (iii) the mean distance between the most correlated altimetry point and tide gauges by 7%.
Also, the number of valid data pairs used to conduct the intercomparison enhances by 0. 1% when using the DT2021 processing all satellites dataset. This highlights the impact of the new DUACS DT2021 reprocessing on the coastal areas, that provides more valid measurements, located closer to the tide gauge sites, compared to DT2018 reprocessing. The new standards and updated geophysical corrections applied to the DUACS DT2021 reprocessing compared to the previous DT2018 version have a direct impact on the observation of coastal ocean sea level in the gridded products. To characterise this impact, the difference between DT2021 and DT2018 consistency is shown in Fig. 2. The spatial distribution of the differences in consistency shows an overall better performance of the DT2021 reprocessing (blue colours) at the connection region between the Baltic Sea and the eastern North Atlantic Ocean; and in most of the Atlantic shore, where an improvement larger than 15% is found for some tide gauge sites.
two satellites SLA dataset We present here the statistics of the intercomparison between the climatic (two satellites) DT2021 and DT2018 processing; and tide gauges. The mean value (Table  2 ) of the rms difference between the DT2018 processing dataset and tide gauges is 4. 41 cm; the variance of the differences (altimetry-tide gauge) is 19 cm 2 ; and the mean distance between the location of the tide gauge and the corresponding altimeter data with the highest correlation is 90 km. If we compare these results with those reported above for the comparison using the DT2021 processing dataset (Table  1 ), we observe that the latter only improves the previous DT2018 reprocessing in terms of the errors with tide gauges, that are reduced by 3%, and the mean distance between the most correlated altimetry point and tide gauges, reduced by 7%; whereas the variance of the differences between the datasets and the number of valid data pairs used to conduct the intercomparison are quite similar.
Such improvements are around 60% lower than those reported for the all satellites datasets. This fact is reflected in the spatial distribution of the differences between DT2021 and DT2018 consistency with tide gauges (figure not shown). We obtain a better performance of the DT2021 reprocessing at the connection region between the Baltic Sea and the eastern North Atlantic Ocean; and in part of the Atlantic shore (coasts of United Kingdom and France). There is a degradation of the DT2021 reprocessing in most of the stations located in the western Mediterranean Sea and the southern coasts of Spain; and in some stations located in the coasts of France, England and Ireland. Also, we found negligible discrepancies between the two reprocessing in the Baltic Sea. This spatial pattern is quite similar to that obtained for the all satellites dataset described above.
Performance of DT2021 reprocessing in monitoring the long term evolution of sea level The computation described above has been conducted by using all available information from the tide gauge dataset, thus including time series of different length spanning from few years to less than three decades (Table  A1  of Appendix A). To assess the performance of DUACS DT2021 processing version in monitoring the long term evolution of sea level in the coastal zone of the European Seas we repeated the analyses described above for a specific time period spanning 20 years: from 1 January 2000 to 31 December 2019. This time period has been chosen because of the largest number of available altimeter missions used to generate the all satellites SLA maps. Tide gauge time series with valid data within such time interval were considered; this allowing the intercomparison altimetry-tide gauges for long term time series with the same length.
Moreover, only tide gauge time series with at least 99% of valid data were used in order to allow the analysis of linear trends. This reduced the original tide gauge dataset to a subset of 27 stations (Tables A1, A2 of Appendix A) mainly located in the northern half of the Baltic Sea (70% of stations) with sparse stations distributed along the coasts of France and Spain (Fig. 3 ). This analysis has been also conducted for the DUACS DT2018 reprocessing for comparison purposes.
245 Linear trends based on monthly observations at each tide gauge site (Fig. 3  and Table  A2  of Appendix A) computed from DUACS DT2021 all satellites dataset (upper panel) show a homogeneous spatial pattern with overall values varying from 2. 30 to 4. 10 mm year -1 in the Baltic and Mediterranean Seas; and between 2. 30 and 3. 30 mm year -1 in the sparse stations located along the North Atlantic European shore, except for the station of SaintMalo that presents a linear trend of 1. 26 mm year -1. Linear trends computed from tide gauges (lower panel) exhibit a more heterogeneous spatial pattern with values ranging 250 between less than 1 mm year -1 for some stations located in the Baltic Sea, and 5. 06 mm year -1 for the station of Barcelona (western Mediterranean Sea). However, most of the tide gauge stations present trend values ranging from 1.
30 to 3 mm year - 1. These results provide further evidence, if needed, the rise in the coastal sea level of the European Seas, this including the westernmost part of the Mediterranean Sea. The differences in trends between the two datasets vary, in absolute values, between near 0 mm year -1 (Brest station, Atlantic French coast) to close to 2. 60 mm year -1 found in the station of Spikarna (Baltic Sea). Linear trends computed from DUACS DT2021 two satellites dataset (figure not shown) exhibit a quite similar spatial pattern with values ranging from 2. 60 to 3. 80 mm year -1 in the Baltic and Mediterranean Seas; and between 2. 40 and 3. 40 mm year -1 along the North Atlantic European coasts. However, some discrepancies between the two datasets are observed. These differences, computed as all satellites minus two satellites datasets, are displayed in Fig.
4. We obtain overall larger linear trends (up to 1 mm year -1 ) for the all satellites dataset in the northernmost and central Baltic Sea as well as in the stations located in the Mediterranean Sea whilst lower values of the same magnitude are mainly observed at the entrance of the Baltic Sea and in most of the stations located along the North Atlantic European shore. On the other hand, linear trends computed from the DT2018 reprocessing (figures not shown) exhibit a quite similar spatial pattern than that reported for the DT2021 processing version with overall values ranging from 2. 20 (2. 40) to 4. 35 (3. 60) mm year -1 in the Baltic and Mediterranean Seas; and between 2. 40 (2. 10) and 3. 05 (2. 85) mm year -1 along the North Atlantic European coasts for the all satellites (two satellites) dataset.
Thus, hardly differences in range are observed between the all satellites dataset from the two reprocessing whereas these differences increase for the two satellites dataset with a lower https://doi. org/10. 5194/egusphere-2023-63 Preprint. Discussion started: 18 January 2023 c Author(s) 2023. CC BY 4. 0 License. variability observed for the DT2018 reprocessing. This fact has an impact on the spatial distribution of the differences between the two processing versions (Fig. 5 ). For the all satellites dataset (upper panel in Fig. 5  values are obtained for the tide gauge stations located in the western Mediterranean Sea and some sparse stations at the entrance of the Baltic Sea. On the contrary, the spatial distribution of the differences between the two reprocessing for the two satellites dataset (lower panel in Fig.
5 ) depicts an homogeneous spatial pattern with overall larger trends for the DT2021 reprocessing except for the tide gauge station of Barseback located in the connection region between the Baltic Sea and the eastern North Atlantic Ocean (Table  A2  in the Appendix A). Fig. 5  also reveals the differences between the two reprocessing, and for the two datasets, when comparing with linear trends from tide gauges: the two satellites dataset from the DT2021 processing version presents larger differences with tide gauges with respect to the DT2018 reprocessing in the whole domain; whilst this is only observed for sparse stations along the North Atlantic shore and the stations located in the Mediterranean Sea for the all satellites dataset. Thus, we obtain closer results between DT2021 reprocessing and tige gauges with respect to the former DT2018 processing version in most of the Baltic Sea region and the stations located along the North Atlantic European coast.
Discussion and conclusions More than 29 years of DUACS Level 3 and Level 4 altimeter data have been recently reprocessed and delivered under the name of DT2021 processing version through the Copernicus Marine Service (all satellites dataset) and the Copernicus Climate Change Service (two satellites dataset). The all satellites SLA products include all the available altimeter missions (ranging from 2 to 7 over the period considered in this study), this making the errors not constant in time since they depend on the number of satellites used. Thus, maps from the all satellites products provide the most accurate sea level estimation with the best spatial and temporal sampling of the ocean at all times. The two satellites SLA dataset is obtained by merging a steady number of altimeters (two) in the satellite constellation.
Compared to the previous DT2018 version, we obtained improvements (reduction) of all satellites dataset of 3% in errors with tide gauges; and 5% in the variance of the differences between the datasets. The mean distance between the most correlated altimetry point and tide gauges reduced by 7%. Also, the number of valid data pairs used to conduct the intercomparison enhanced by 0. 1% when using the DT2021 processing. This highlights the impact of the new DUACS DT2021 version on the coastal areas, that provides more valid measurements, located closer to the tide gauge sites, compared to DT2018 reprocessing. On the other hand, we found an overall poorer improvement of the DT2021 two satellites dataset over the previous reprocessing: errors with tide gauges were reduced by https://doi. org/10.
5194/egusphere-2023-63 Preprint. Discussion started: 18 January 2023 c Author(s) 2023. CC BY 4. 0 License. 1%, and the mean distance between the most correlated altimetry point and tide gauges was reduced by 3%. The variance of the differences between the datasets and the number of valid data pairs used to conduct the intercomparison were quite similar. These improvements are around 60% lower than those reported for the all satellites datasets. This fact could be explained by differences in the mapping parameters used for the two products: DT2021 mapping parameters (i. e. spatial and temporal correlation scales; a priori errors on the measurements) are evolved in CMEMS products (CMEMS QUID, 2022) with the objective to better retrieve mesoscale signals; whilst no evolution of the mapping parameter was implemented in C3S DT2021 product (C3S PUG, 2022).
The quality assessment of DUACS DT2021 reprocessing revealed a better performance of the all satellites products in the retrieval of SSH in the coastal zone with respect to the two satellites products for the time period investigated (27 years). Namely, we obtained a reduction of 5% in errors with tide gauges and 10% in variance difference between altimetry and tide gauges when using the all satellites dataset with respect to the two satellites product. This is because despite the larger stability of the two satellites dataset, this product is optimised for climatic signal. Thus, it is less performant for higher frequency signals. In this context (analysis of high frequency signals), the results reported here show that the all satellites dataset should be considered for the analysis of long time series of SSH in the coastal zone of the European Seas.
This can be clearly seen in Fig. 6  showing the differences (computed as all satellites minus two satellites datasets) for consistency between altimetry and tide gauges. not accurate enough in the coastal zone. In any case, the linear trends for the tide gauge of Barcelona described above are of the same order of magnitude than those reported by  Taibi and Haddad (2019)  computed for the time period spanning from 1993 to 2015 (linear trend of 2. 74 mm year -1 for altimetry; 6. 73 mm year -1 for the tide gauge; trend difference of 3. 99 mm year - 1 ), thus supporting the results obtained here. The intercomparison conducted here between L4 gridded products from the new DUACS DT2021 release and the DT2018 version previously available; and tide gauges have demonstrated the better performance of the new DT2021 version in the retrieval of sea level in the coastal zone of the European Seas.
Furthermore, the all satellites dataset provided more accurate sea level measurements when comparing with tide gauges respect to the climatic two satellites dataset due to the better performance of the former for the assessment of higher than climatic frequency signals. On the opposite, when analysing linear trends from 20 year long time series we found the two satellite dataset the most suitable product for the assessment of long term sea level time series in the coastal zone due to its larger stability to the detriment of the CMEMS all satellites dataset.
Acknowledgement This study has been conducted in the frame of the  Copernicus Marine Service SL-TAC and In Situ TAC (INS-TAC)  projects. The  Copernicus Marine Service , led by  Mercator-Ocean , is based on a distributed model of service production, relying on the expertise of a wide network of participating European organisations involved in operational oceanography. We acknowledge the regional in situ data production centres responsible for the collection and distribution of the tide gauge data used in this study:  Puertos del Estado (Spain)  for the  Iberia-Biscay-Ireland region ;  HCMR (Greece)  for the  Mediterranean Sea ;  IMR (Norway)  for the Arctic;  SMHI (Sweden)  for the  Baltic Sea ;  BSH (Germany)  for the  North-West Shelves region ;  Coriolis (France)  for the global ocean. This work represents a contribution to  CSIC Interdisciplinary Thematic Platform (PTI) Teledetección.
Data availability Altimetry datasets are available from the Copernicus Marine Service web portal (https://resources. marine. copernicus. eu/products/, last access: 15 July 2022). Tide gauge measurements are available from the Copernicus Marine INS-TAC data repository web portal (www. marineinsitu. eu, last access: 3 June 2022). Tide gauge data are provided by the following regional in situ data production centres: Puertos del Estado (Spain) for the Iberia-Biscay-Ireland region; HCMR (Greece) for the Mediterranean Sea; IMR (Norway) for the Arctic; SMHI (Sweden) for the Baltic Sea; BSH (Germany) for the North West Shelves region; Coriolis (France) for the global ocean. The ancillary data used to obtain the Dynamic Atmospheric Correction applied to the altimetry grid point closest to the tide gauge locations are available at the AVISO webpage: https://www.
(western Mediterranean) and the southwestern part of Norway where values between 5% and 15% are obtained. The consistency between the DUACS DT2021 two satellites dataset and tide gauges (figure not shown) presents a quite similar spatial pattern and results. These outcomes improve the ones reported in Sánchez-Román et al. (2020) from the intercomparison conducted between Sentinel-3A L3 along track DUACS DT2018 dataset and tide gauge measurements in the region computed over a period of two and half years. https://doi. org/10. 5194/egusphere-2023-63 Preprint. Discussion started: 18 January 2023 c Author(s) 2023. CC BY 4. 0 License.
satellites dataset two satellites dataset all satellites improvement rms diff. (cm) 4. 11 (0. 01) 4. 35 (0. 01) 5 % var TG (cm 2 ) 89 (1) var ALT (cm 2 ) 81 (1) 79 (1) var TG-ALT (cm 2 ) 17 (1) 19 (1) 10 % data pairs 1. 163. 588 1. 161. 315 0. 2 % stations 213 Distance TG (km) 82 87 6 %.
Evidence for widespread continued movement on other shortening structures has been lacking. In this Article, we present the results of a global survey of grabens atop shortening structures (Figs. 1  and 2  and Supplementary Data 1). These shortening structures are a continuum of landforms accepted to be the surface manifestations of thrust faults and folding formed by lithospheric horizontal compression  13, 19. We first mapped all potential shortening structures to produce a global tectonics database with a high level of spatial detail suitable for our study (Fig. 3a  and Supplementary Data 2). Next, we filtered and processed all NAC frames with spatial resolution of 150 m pixel -1 or better that intersected the structures in our database. We then searched for and catalogued the occurrence of grabens in these NAC frames.
In total, there are 727 grabens, of which we identify 190 as 'certain' (Figs. 2  and  3 and Supplementary Data 1). The certain grabens are widely distributed (Figs. 2  and 3 , Extended Data Fig. 6  and Supplementary Data 1). They can be found in 13 out of 15 of Mercury's mapping quadrangles (Fig. 2c ), from 72° N to 76° S, and occur on 48 individual shortening structures, forming a dataset comprising 131 digitized lines. Another 244 structures host 'probable' grabens (Figs. 2  and 3  and Supplementary Data 1). It is likely that many, or even most, of these probable grabens are real, but the current image quality is insufficient for us to be certain in any individual case. We therefore base our analysis and conclusions solely on certain grabens.
Length, depth and age of grabens The texturally sharp morphology of the grabens we have identified suggests these are very young features compared with most landforms on Mercury. To further demonstrate that the grabens are geologically young, we undertook shadow measurements of all certain grabens covered by NAC frames that have measurable shadows. This allowed us to estimate the depths of the grabens and compare them with terrestrial and lunar analogues. We were able to make 331 shadow-based depth calculations for 99 of the certain grabens on 29 of 48 shortening structures (Extended Data Fig. 7  and Supplementary Data 3). Of the 331 measurements, 220 were less than 50 m deep, 92 were 50-100 m deep and 19 were >100 m deep. The age of each graben can be estimated on the basis of an assumed rate of infilling and an estimated original graben depth.
Article https://doi. org/10. 1038/s41561-023-01281-5 investigation. We expect that the improved image data resolution and coverage that will be provided by the BepiColombo spacecraft  50  , which will enter orbit around Mercury in 2025, will mean many more of these landforms will be confirmed or discovered. Analysis of that dataset will help better constrain models of Mercury's evolution by better measuring the population and spatial distribution of these recent tectonic features.
Data To produce the global shortening structure map (Fig. 3a  and Supplementary Data 2) for the primary basemap we used the MESSEN-GER version 1. 0 monochrome, moderate solar incidence angle (~74°) Map-Projected Basemap Reduced Data Record. This global mosaic has a resolution of ~166 m pixel -1 (256 pixels per degree) and was used alongside ancillary MDIS data products: monochrome high-incidence-angle (~78°) tiles (~166 m pixel -1 ) and the Mercury Laser Altimeter and stereo-derived DEMs  52. These products were sourced from the Planetary Data System Cartography and Imaging Sciences Node. For the grabens survey, we used NAC frames obtained from the Mercury Orbital Data Explorer as part of the NASA (National Aeronautics and Space Administration) Planetary Data System Geosciences node. We processed all data using the United States Geological Survey ISIS3 (Integrated Software for Imagers and Spectrometers version 3) to produce map-projected data for ingestion into ArcGIS for analysis.